src/web/api/queries/query.c
// SPDX-License-Identifier: GPL-3.0-or-later
#include "query.h"
#include "web/api/formatters/rrd2json.h"
#include "rrdr.h"
#include "average/average.h"
#include "countif/countif.h"
#include "incremental_sum/incremental_sum.h"
#include "max/max.h"
#include "median/median.h"
#include "min/min.h"
#include "sum/sum.h"
#include "stddev/stddev.h"
#include "ses/ses.h"
#include "des/des.h"
#include "percentile/percentile.h"
#include "trimmed_mean/trimmed_mean.h"
#define QUERY_PLAN_MIN_POINTS 10
#define POINTS_TO_EXPAND_QUERY 5
// ----------------------------------------------------------------------------
static struct {
const char *name;
uint32_t hash;
RRDR_TIME_GROUPING value;
RRDR_TIME_GROUPING add_flush;
// One time initialization for the module.
// This is called once, when netdata starts.
void (*init)(void);
// Allocate all required structures for a query.
// This is called once for each netdata query.
void (*create)(struct rrdresult *r, const char *options);
// Cleanup collected values, but don't destroy the structures.
// This is called when the query engine switches dimensions,
// as part of the same query (so same chart, switching metric).
void (*reset)(struct rrdresult *r);
// Free all resources allocated for the query.
void (*free)(struct rrdresult *r);
// Add a single value into the calculation.
// The module may decide to cache it, or use it in the fly.
void (*add)(struct rrdresult *r, NETDATA_DOUBLE value);
// Generate a single result for the values added so far.
// More values and points may be requested later.
// It is up to the module to reset its internal structures
// when flushing it (so for a few modules it may be better to
// continue after a flush as if nothing changed, for others a
// cleanup of the internal structures may be required).
NETDATA_DOUBLE (*flush)(struct rrdresult *r, RRDR_VALUE_FLAGS *rrdr_value_options_ptr);
TIER_QUERY_FETCH tier_query_fetch;
} api_v1_data_groups[] = {
{.name = "average",
.hash = 0,
.value = RRDR_GROUPING_AVERAGE,
.add_flush = RRDR_GROUPING_AVERAGE,
.init = NULL,
.create= tg_average_create,
.reset = tg_average_reset,
.free = tg_average_free,
.add = tg_average_add,
.flush = tg_average_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "avg", // alias on 'average'
.hash = 0,
.value = RRDR_GROUPING_AVERAGE,
.add_flush = RRDR_GROUPING_AVERAGE,
.init = NULL,
.create= tg_average_create,
.reset = tg_average_reset,
.free = tg_average_free,
.add = tg_average_add,
.flush = tg_average_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "mean", // alias on 'average'
.hash = 0,
.value = RRDR_GROUPING_AVERAGE,
.add_flush = RRDR_GROUPING_AVERAGE,
.init = NULL,
.create= tg_average_create,
.reset = tg_average_reset,
.free = tg_average_free,
.add = tg_average_add,
.flush = tg_average_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean1",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN1,
.add_flush = RRDR_GROUPING_TRIMMED_MEAN,
.init = NULL,
.create= tg_trimmed_mean_create_1,
.reset = tg_trimmed_mean_reset,
.free = tg_trimmed_mean_free,
.add = tg_trimmed_mean_add,
.flush = tg_trimmed_mean_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean2",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN2,
.add_flush = RRDR_GROUPING_TRIMMED_MEAN,
.init = NULL,
.create= tg_trimmed_mean_create_2,
.reset = tg_trimmed_mean_reset,
.free = tg_trimmed_mean_free,
.add = tg_trimmed_mean_add,
.flush = tg_trimmed_mean_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean3",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN3,
.add_flush = RRDR_GROUPING_TRIMMED_MEAN,
.init = NULL,
.create= tg_trimmed_mean_create_3,
.reset = tg_trimmed_mean_reset,
.free = tg_trimmed_mean_free,
.add = tg_trimmed_mean_add,
.flush = tg_trimmed_mean_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean5",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN,
.add_flush = RRDR_GROUPING_TRIMMED_MEAN,
.init = NULL,
.create= tg_trimmed_mean_create_5,
.reset = tg_trimmed_mean_reset,
.free = tg_trimmed_mean_free,
.add = tg_trimmed_mean_add,
.flush = tg_trimmed_mean_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean10",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN10,
.add_flush = RRDR_GROUPING_TRIMMED_MEAN,
.init = NULL,
.create= tg_trimmed_mean_create_10,
.reset = tg_trimmed_mean_reset,
.free = tg_trimmed_mean_free,
.add = tg_trimmed_mean_add,
.flush = tg_trimmed_mean_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean15",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN15,
.add_flush = RRDR_GROUPING_TRIMMED_MEAN,
.init = NULL,
.create= tg_trimmed_mean_create_15,
.reset = tg_trimmed_mean_reset,
.free = tg_trimmed_mean_free,
.add = tg_trimmed_mean_add,
.flush = tg_trimmed_mean_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean20",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN20,
.add_flush = RRDR_GROUPING_TRIMMED_MEAN,
.init = NULL,
.create= tg_trimmed_mean_create_20,
.reset = tg_trimmed_mean_reset,
.free = tg_trimmed_mean_free,
.add = tg_trimmed_mean_add,
.flush = tg_trimmed_mean_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean25",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN25,
.add_flush = RRDR_GROUPING_TRIMMED_MEAN,
.init = NULL,
.create= tg_trimmed_mean_create_25,
.reset = tg_trimmed_mean_reset,
.free = tg_trimmed_mean_free,
.add = tg_trimmed_mean_add,
.flush = tg_trimmed_mean_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN,
.add_flush = RRDR_GROUPING_TRIMMED_MEAN,
.init = NULL,
.create= tg_trimmed_mean_create_5,
.reset = tg_trimmed_mean_reset,
.free = tg_trimmed_mean_free,
.add = tg_trimmed_mean_add,
.flush = tg_trimmed_mean_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "incremental_sum",
.hash = 0,
.value = RRDR_GROUPING_INCREMENTAL_SUM,
.add_flush = RRDR_GROUPING_INCREMENTAL_SUM,
.init = NULL,
.create= tg_incremental_sum_create,
.reset = tg_incremental_sum_reset,
.free = tg_incremental_sum_free,
.add = tg_incremental_sum_add,
.flush = tg_incremental_sum_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "incremental-sum",
.hash = 0,
.value = RRDR_GROUPING_INCREMENTAL_SUM,
.add_flush = RRDR_GROUPING_INCREMENTAL_SUM,
.init = NULL,
.create= tg_incremental_sum_create,
.reset = tg_incremental_sum_reset,
.free = tg_incremental_sum_free,
.add = tg_incremental_sum_add,
.flush = tg_incremental_sum_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "median",
.hash = 0,
.value = RRDR_GROUPING_MEDIAN,
.add_flush = RRDR_GROUPING_MEDIAN,
.init = NULL,
.create= tg_median_create,
.reset = tg_median_reset,
.free = tg_median_free,
.add = tg_median_add,
.flush = tg_median_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median1",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN1,
.add_flush = RRDR_GROUPING_MEDIAN,
.init = NULL,
.create= tg_median_create_trimmed_1,
.reset = tg_median_reset,
.free = tg_median_free,
.add = tg_median_add,
.flush = tg_median_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median2",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN2,
.add_flush = RRDR_GROUPING_MEDIAN,
.init = NULL,
.create= tg_median_create_trimmed_2,
.reset = tg_median_reset,
.free = tg_median_free,
.add = tg_median_add,
.flush = tg_median_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median3",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN3,
.add_flush = RRDR_GROUPING_MEDIAN,
.init = NULL,
.create= tg_median_create_trimmed_3,
.reset = tg_median_reset,
.free = tg_median_free,
.add = tg_median_add,
.flush = tg_median_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median5",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN,
.add_flush = RRDR_GROUPING_MEDIAN,
.init = NULL,
.create= tg_median_create_trimmed_5,
.reset = tg_median_reset,
.free = tg_median_free,
.add = tg_median_add,
.flush = tg_median_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median10",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN10,
.add_flush = RRDR_GROUPING_MEDIAN,
.init = NULL,
.create= tg_median_create_trimmed_10,
.reset = tg_median_reset,
.free = tg_median_free,
.add = tg_median_add,
.flush = tg_median_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median15",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN15,
.add_flush = RRDR_GROUPING_MEDIAN,
.init = NULL,
.create= tg_median_create_trimmed_15,
.reset = tg_median_reset,
.free = tg_median_free,
.add = tg_median_add,
.flush = tg_median_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median20",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN20,
.add_flush = RRDR_GROUPING_MEDIAN,
.init = NULL,
.create= tg_median_create_trimmed_20,
.reset = tg_median_reset,
.free = tg_median_free,
.add = tg_median_add,
.flush = tg_median_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median25",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN25,
.add_flush = RRDR_GROUPING_MEDIAN,
.init = NULL,
.create= tg_median_create_trimmed_25,
.reset = tg_median_reset,
.free = tg_median_free,
.add = tg_median_add,
.flush = tg_median_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN,
.add_flush = RRDR_GROUPING_MEDIAN,
.init = NULL,
.create= tg_median_create_trimmed_5,
.reset = tg_median_reset,
.free = tg_median_free,
.add = tg_median_add,
.flush = tg_median_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile25",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE25,
.add_flush = RRDR_GROUPING_PERCENTILE,
.init = NULL,
.create= tg_percentile_create_25,
.reset = tg_percentile_reset,
.free = tg_percentile_free,
.add = tg_percentile_add,
.flush = tg_percentile_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile50",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE50,
.add_flush = RRDR_GROUPING_PERCENTILE,
.init = NULL,
.create= tg_percentile_create_50,
.reset = tg_percentile_reset,
.free = tg_percentile_free,
.add = tg_percentile_add,
.flush = tg_percentile_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile75",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE75,
.add_flush = RRDR_GROUPING_PERCENTILE,
.init = NULL,
.create= tg_percentile_create_75,
.reset = tg_percentile_reset,
.free = tg_percentile_free,
.add = tg_percentile_add,
.flush = tg_percentile_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile80",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE80,
.add_flush = RRDR_GROUPING_PERCENTILE,
.init = NULL,
.create= tg_percentile_create_80,
.reset = tg_percentile_reset,
.free = tg_percentile_free,
.add = tg_percentile_add,
.flush = tg_percentile_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile90",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE90,
.add_flush = RRDR_GROUPING_PERCENTILE,
.init = NULL,
.create= tg_percentile_create_90,
.reset = tg_percentile_reset,
.free = tg_percentile_free,
.add = tg_percentile_add,
.flush = tg_percentile_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile95",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE,
.add_flush = RRDR_GROUPING_PERCENTILE,
.init = NULL,
.create= tg_percentile_create_95,
.reset = tg_percentile_reset,
.free = tg_percentile_free,
.add = tg_percentile_add,
.flush = tg_percentile_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile97",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE97,
.add_flush = RRDR_GROUPING_PERCENTILE,
.init = NULL,
.create= tg_percentile_create_97,
.reset = tg_percentile_reset,
.free = tg_percentile_free,
.add = tg_percentile_add,
.flush = tg_percentile_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile98",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE98,
.add_flush = RRDR_GROUPING_PERCENTILE,
.init = NULL,
.create= tg_percentile_create_98,
.reset = tg_percentile_reset,
.free = tg_percentile_free,
.add = tg_percentile_add,
.flush = tg_percentile_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile99",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE99,
.add_flush = RRDR_GROUPING_PERCENTILE,
.init = NULL,
.create= tg_percentile_create_99,
.reset = tg_percentile_reset,
.free = tg_percentile_free,
.add = tg_percentile_add,
.flush = tg_percentile_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE,
.add_flush = RRDR_GROUPING_PERCENTILE,
.init = NULL,
.create= tg_percentile_create_95,
.reset = tg_percentile_reset,
.free = tg_percentile_free,
.add = tg_percentile_add,
.flush = tg_percentile_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "min",
.hash = 0,
.value = RRDR_GROUPING_MIN,
.add_flush = RRDR_GROUPING_MIN,
.init = NULL,
.create= tg_min_create,
.reset = tg_min_reset,
.free = tg_min_free,
.add = tg_min_add,
.flush = tg_min_flush,
.tier_query_fetch = TIER_QUERY_FETCH_MIN
},
{.name = "max",
.hash = 0,
.value = RRDR_GROUPING_MAX,
.add_flush = RRDR_GROUPING_MAX,
.init = NULL,
.create= tg_max_create,
.reset = tg_max_reset,
.free = tg_max_free,
.add = tg_max_add,
.flush = tg_max_flush,
.tier_query_fetch = TIER_QUERY_FETCH_MAX
},
{.name = "sum",
.hash = 0,
.value = RRDR_GROUPING_SUM,
.add_flush = RRDR_GROUPING_SUM,
.init = NULL,
.create= tg_sum_create,
.reset = tg_sum_reset,
.free = tg_sum_free,
.add = tg_sum_add,
.flush = tg_sum_flush,
.tier_query_fetch = TIER_QUERY_FETCH_SUM
},
// standard deviation
{.name = "stddev",
.hash = 0,
.value = RRDR_GROUPING_STDDEV,
.add_flush = RRDR_GROUPING_STDDEV,
.init = NULL,
.create= tg_stddev_create,
.reset = tg_stddev_reset,
.free = tg_stddev_free,
.add = tg_stddev_add,
.flush = tg_stddev_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "cv", // coefficient variation is calculated by stddev
.hash = 0,
.value = RRDR_GROUPING_CV,
.add_flush = RRDR_GROUPING_CV,
.init = NULL,
.create= tg_stddev_create, // not an error, stddev calculates this too
.reset = tg_stddev_reset, // not an error, stddev calculates this too
.free = tg_stddev_free, // not an error, stddev calculates this too
.add = tg_stddev_add, // not an error, stddev calculates this too
.flush = tg_stddev_coefficient_of_variation_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "rsd", // alias of 'cv'
.hash = 0,
.value = RRDR_GROUPING_CV,
.add_flush = RRDR_GROUPING_CV,
.init = NULL,
.create= tg_stddev_create, // not an error, stddev calculates this too
.reset = tg_stddev_reset, // not an error, stddev calculates this too
.free = tg_stddev_free, // not an error, stddev calculates this too
.add = tg_stddev_add, // not an error, stddev calculates this too
.flush = tg_stddev_coefficient_of_variation_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
// single exponential smoothing
{.name = "ses",
.hash = 0,
.value = RRDR_GROUPING_SES,
.add_flush = RRDR_GROUPING_SES,
.init = tg_ses_init,
.create= tg_ses_create,
.reset = tg_ses_reset,
.free = tg_ses_free,
.add = tg_ses_add,
.flush = tg_ses_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "ema", // alias for 'ses'
.hash = 0,
.value = RRDR_GROUPING_SES,
.add_flush = RRDR_GROUPING_SES,
.init = NULL,
.create= tg_ses_create,
.reset = tg_ses_reset,
.free = tg_ses_free,
.add = tg_ses_add,
.flush = tg_ses_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "ewma", // alias for ses
.hash = 0,
.value = RRDR_GROUPING_SES,
.add_flush = RRDR_GROUPING_SES,
.init = NULL,
.create= tg_ses_create,
.reset = tg_ses_reset,
.free = tg_ses_free,
.add = tg_ses_add,
.flush = tg_ses_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
// double exponential smoothing
{.name = "des",
.hash = 0,
.value = RRDR_GROUPING_DES,
.add_flush = RRDR_GROUPING_DES,
.init = tg_des_init,
.create= tg_des_create,
.reset = tg_des_reset,
.free = tg_des_free,
.add = tg_des_add,
.flush = tg_des_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "countif",
.hash = 0,
.value = RRDR_GROUPING_COUNTIF,
.add_flush = RRDR_GROUPING_COUNTIF,
.init = NULL,
.create= tg_countif_create,
.reset = tg_countif_reset,
.free = tg_countif_free,
.add = tg_countif_add,
.flush = tg_countif_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
// terminator
{.name = NULL,
.hash = 0,
.value = RRDR_GROUPING_UNDEFINED,
.add_flush = RRDR_GROUPING_AVERAGE,
.init = NULL,
.create= tg_average_create,
.reset = tg_average_reset,
.free = tg_average_free,
.add = tg_average_add,
.flush = tg_average_flush,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
}
};
void time_grouping_init(void) {
int i;
for(i = 0; api_v1_data_groups[i].name ; i++) {
api_v1_data_groups[i].hash = simple_hash(api_v1_data_groups[i].name);
if(api_v1_data_groups[i].init)
api_v1_data_groups[i].init();
}
}
const char *time_grouping_id2txt(RRDR_TIME_GROUPING group) {
int i;
for(i = 0; api_v1_data_groups[i].name ; i++) {
if(api_v1_data_groups[i].value == group) {
return api_v1_data_groups[i].name;
}
}
return "average";
}
RRDR_TIME_GROUPING time_grouping_txt2id(const char *name) {
int i;
uint32_t hash = simple_hash(name);
for(i = 0; api_v1_data_groups[i].name ; i++)
if(unlikely(hash == api_v1_data_groups[i].hash && !strcmp(name, api_v1_data_groups[i].name)))
return api_v1_data_groups[i].value;
return RRDR_GROUPING_AVERAGE;
}
RRDR_TIME_GROUPING time_grouping_parse(const char *name, RRDR_TIME_GROUPING def) {
int i;
uint32_t hash = simple_hash(name);
for(i = 0; api_v1_data_groups[i].name ; i++)
if(unlikely(hash == api_v1_data_groups[i].hash && !strcmp(name, api_v1_data_groups[i].name)))
return api_v1_data_groups[i].value;
return def;
}
const char *time_grouping_tostring(RRDR_TIME_GROUPING group) {
int i;
for(i = 0; api_v1_data_groups[i].name ; i++)
if(unlikely(group == api_v1_data_groups[i].value))
return api_v1_data_groups[i].name;
return "unknown";
}
static void rrdr_set_grouping_function(RRDR *r, RRDR_TIME_GROUPING group_method) {
int i, found = 0;
for(i = 0; !found && api_v1_data_groups[i].name ;i++) {
if(api_v1_data_groups[i].value == group_method) {
r->time_grouping.create = api_v1_data_groups[i].create;
r->time_grouping.reset = api_v1_data_groups[i].reset;
r->time_grouping.free = api_v1_data_groups[i].free;
r->time_grouping.add = api_v1_data_groups[i].add;
r->time_grouping.flush = api_v1_data_groups[i].flush;
r->time_grouping.tier_query_fetch = api_v1_data_groups[i].tier_query_fetch;
r->time_grouping.add_flush = api_v1_data_groups[i].add_flush;
found = 1;
}
}
if(!found) {
errno = 0;
internal_error(true, "QUERY: grouping method %u not found. Using 'average'", (unsigned int)group_method);
r->time_grouping.create = tg_average_create;
r->time_grouping.reset = tg_average_reset;
r->time_grouping.free = tg_average_free;
r->time_grouping.add = tg_average_add;
r->time_grouping.flush = tg_average_flush;
r->time_grouping.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE;
r->time_grouping.add_flush = RRDR_GROUPING_AVERAGE;
}
}
static inline void time_grouping_add(RRDR *r, NETDATA_DOUBLE value, const RRDR_TIME_GROUPING add_flush) {
switch(add_flush) {
case RRDR_GROUPING_AVERAGE:
tg_average_add(r, value);
break;
case RRDR_GROUPING_MAX:
tg_max_add(r, value);
break;
case RRDR_GROUPING_MIN:
tg_min_add(r, value);
break;
case RRDR_GROUPING_MEDIAN:
tg_median_add(r, value);
break;
case RRDR_GROUPING_STDDEV:
case RRDR_GROUPING_CV:
tg_stddev_add(r, value);
break;
case RRDR_GROUPING_SUM:
tg_sum_add(r, value);
break;
case RRDR_GROUPING_COUNTIF:
tg_countif_add(r, value);
break;
case RRDR_GROUPING_TRIMMED_MEAN:
tg_trimmed_mean_add(r, value);
break;
case RRDR_GROUPING_PERCENTILE:
tg_percentile_add(r, value);
break;
case RRDR_GROUPING_SES:
tg_ses_add(r, value);
break;
case RRDR_GROUPING_DES:
tg_des_add(r, value);
break;
case RRDR_GROUPING_INCREMENTAL_SUM:
tg_incremental_sum_add(r, value);
break;
default:
r->time_grouping.add(r, value);
break;
}
}
static inline NETDATA_DOUBLE time_grouping_flush(RRDR *r, RRDR_VALUE_FLAGS *rrdr_value_options_ptr, const RRDR_TIME_GROUPING add_flush) {
switch(add_flush) {
case RRDR_GROUPING_AVERAGE:
return tg_average_flush(r, rrdr_value_options_ptr);
case RRDR_GROUPING_MAX:
return tg_max_flush(r, rrdr_value_options_ptr);
case RRDR_GROUPING_MIN:
return tg_min_flush(r, rrdr_value_options_ptr);
case RRDR_GROUPING_MEDIAN:
return tg_median_flush(r, rrdr_value_options_ptr);
case RRDR_GROUPING_STDDEV:
return tg_stddev_flush(r, rrdr_value_options_ptr);
case RRDR_GROUPING_CV:
return tg_stddev_coefficient_of_variation_flush(r, rrdr_value_options_ptr);
case RRDR_GROUPING_SUM:
return tg_sum_flush(r, rrdr_value_options_ptr);
case RRDR_GROUPING_COUNTIF:
return tg_countif_flush(r, rrdr_value_options_ptr);
case RRDR_GROUPING_TRIMMED_MEAN:
return tg_trimmed_mean_flush(r, rrdr_value_options_ptr);
case RRDR_GROUPING_PERCENTILE:
return tg_percentile_flush(r, rrdr_value_options_ptr);
case RRDR_GROUPING_SES:
return tg_ses_flush(r, rrdr_value_options_ptr);
case RRDR_GROUPING_DES:
return tg_des_flush(r, rrdr_value_options_ptr);
case RRDR_GROUPING_INCREMENTAL_SUM:
return tg_incremental_sum_flush(r, rrdr_value_options_ptr);
default:
return r->time_grouping.flush(r, rrdr_value_options_ptr);
}
}
RRDR_GROUP_BY group_by_parse(char *s) {
RRDR_GROUP_BY group_by = RRDR_GROUP_BY_NONE;
while(s) {
char *key = strsep_skip_consecutive_separators(&s, ",| ");
if (!key || !*key) continue;
if (strcmp(key, "selected") == 0)
group_by |= RRDR_GROUP_BY_SELECTED;
if (strcmp(key, "dimension") == 0)
group_by |= RRDR_GROUP_BY_DIMENSION;
if (strcmp(key, "instance") == 0)
group_by |= RRDR_GROUP_BY_INSTANCE;
if (strcmp(key, "percentage-of-instance") == 0)
group_by |= RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE;
if (strcmp(key, "label") == 0)
group_by |= RRDR_GROUP_BY_LABEL;
if (strcmp(key, "node") == 0)
group_by |= RRDR_GROUP_BY_NODE;
if (strcmp(key, "context") == 0)
group_by |= RRDR_GROUP_BY_CONTEXT;
if (strcmp(key, "units") == 0)
group_by |= RRDR_GROUP_BY_UNITS;
}
if((group_by & RRDR_GROUP_BY_SELECTED) && (group_by & ~RRDR_GROUP_BY_SELECTED)) {
internal_error(true, "group-by given by query has 'selected' together with more groupings");
group_by = RRDR_GROUP_BY_SELECTED; // remove all other groupings
}
if(group_by & RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE)
group_by = RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE; // remove all other groupings
return group_by;
}
void buffer_json_group_by_to_array(BUFFER *wb, RRDR_GROUP_BY group_by) {
if(group_by == RRDR_GROUP_BY_NONE)
buffer_json_add_array_item_string(wb, "none");
else {
if (group_by & RRDR_GROUP_BY_DIMENSION)
buffer_json_add_array_item_string(wb, "dimension");
if (group_by & RRDR_GROUP_BY_INSTANCE)
buffer_json_add_array_item_string(wb, "instance");
if (group_by & RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE)
buffer_json_add_array_item_string(wb, "percentage-of-instance");
if (group_by & RRDR_GROUP_BY_LABEL)
buffer_json_add_array_item_string(wb, "label");
if (group_by & RRDR_GROUP_BY_NODE)
buffer_json_add_array_item_string(wb, "node");
if (group_by & RRDR_GROUP_BY_CONTEXT)
buffer_json_add_array_item_string(wb, "context");
if (group_by & RRDR_GROUP_BY_UNITS)
buffer_json_add_array_item_string(wb, "units");
if (group_by & RRDR_GROUP_BY_SELECTED)
buffer_json_add_array_item_string(wb, "selected");
}
}
RRDR_GROUP_BY_FUNCTION group_by_aggregate_function_parse(const char *s) {
if(strcmp(s, "average") == 0)
return RRDR_GROUP_BY_FUNCTION_AVERAGE;
if(strcmp(s, "avg") == 0)
return RRDR_GROUP_BY_FUNCTION_AVERAGE;
if(strcmp(s, "min") == 0)
return RRDR_GROUP_BY_FUNCTION_MIN;
if(strcmp(s, "max") == 0)
return RRDR_GROUP_BY_FUNCTION_MAX;
if(strcmp(s, "sum") == 0)
return RRDR_GROUP_BY_FUNCTION_SUM;
if(strcmp(s, "percentage") == 0)
return RRDR_GROUP_BY_FUNCTION_PERCENTAGE;
return RRDR_GROUP_BY_FUNCTION_AVERAGE;
}
const char *group_by_aggregate_function_to_string(RRDR_GROUP_BY_FUNCTION group_by_function) {
switch(group_by_function) {
default:
case RRDR_GROUP_BY_FUNCTION_AVERAGE:
return "average";
case RRDR_GROUP_BY_FUNCTION_MIN:
return "min";
case RRDR_GROUP_BY_FUNCTION_MAX:
return "max";
case RRDR_GROUP_BY_FUNCTION_SUM:
return "sum";
case RRDR_GROUP_BY_FUNCTION_PERCENTAGE:
return "percentage";
}
}
// ----------------------------------------------------------------------------
// helpers to find our way in RRDR
static inline RRDR_VALUE_FLAGS *UNUSED_FUNCTION(rrdr_line_options)(RRDR *r, long rrdr_line) {
return &r->o[ rrdr_line * r->d ];
}
static inline NETDATA_DOUBLE *UNUSED_FUNCTION(rrdr_line_values)(RRDR *r, long rrdr_line) {
return &r->v[ rrdr_line * r->d ];
}
static inline long rrdr_line_init(RRDR *r __maybe_unused, time_t t __maybe_unused, long rrdr_line) {
rrdr_line++;
internal_fatal(rrdr_line >= (long)r->n,
"QUERY: requested to step above RRDR size for query '%s'",
r->internal.qt->id);
internal_fatal(r->t[rrdr_line] != t,
"QUERY: wrong timestamp at RRDR line %ld, expected %ld, got %ld, of query '%s'",
rrdr_line, r->t[rrdr_line], t, r->internal.qt->id);
return rrdr_line;
}
// ----------------------------------------------------------------------------
// tier management
static bool query_metric_is_valid_tier(QUERY_METRIC *qm, size_t tier) {
if(!qm->tiers[tier].smh || !qm->tiers[tier].db_first_time_s || !qm->tiers[tier].db_last_time_s || !qm->tiers[tier].db_update_every_s)
return false;
return true;
}
static size_t query_metric_first_working_tier(QUERY_METRIC *qm) {
for(size_t tier = 0; tier < storage_tiers ; tier++) {
// find the db time-range for this tier for all metrics
STORAGE_METRIC_HANDLE *smh = qm->tiers[tier].smh;
time_t first_time_s = qm->tiers[tier].db_first_time_s;
time_t last_time_s = qm->tiers[tier].db_last_time_s;
time_t update_every_s = qm->tiers[tier].db_update_every_s;
if(!smh || !first_time_s || !last_time_s || !update_every_s)
continue;
return tier;
}
return 0;
}
static long query_plan_points_coverage_weight(time_t db_first_time_s, time_t db_last_time_s, time_t db_update_every_s, time_t after_wanted, time_t before_wanted, size_t points_wanted, size_t tier __maybe_unused) {
if(db_first_time_s == 0 ||
db_last_time_s == 0 ||
db_update_every_s == 0 ||
db_first_time_s > before_wanted ||
db_last_time_s < after_wanted)
return -LONG_MAX;
long long common_first_t = MAX(db_first_time_s, after_wanted);
long long common_last_t = MIN(db_last_time_s, before_wanted);
long long time_coverage = (common_last_t - common_first_t) * 1000000LL / (before_wanted - after_wanted);
long long points_wanted_in_coverage = (long long)points_wanted * time_coverage / 1000000LL;
long long points_available = (common_last_t - common_first_t) / db_update_every_s;
long long points_delta = (long)(points_available - points_wanted_in_coverage);
long long points_coverage = (points_delta < 0) ? (long)(points_available * time_coverage / points_wanted_in_coverage) : time_coverage;
// a way to benefit higher tiers
// points_coverage += (long)tier * 10000;
if(points_available <= 0)
return -LONG_MAX;
return (long)(points_coverage + (25000LL * tier)); // 2.5% benefit for each higher tier
}
static size_t query_metric_best_tier_for_timeframe(QUERY_METRIC *qm, time_t after_wanted, time_t before_wanted, size_t points_wanted) {
if(unlikely(storage_tiers < 2))
return 0;
if(unlikely(after_wanted == before_wanted || points_wanted <= 0))
return query_metric_first_working_tier(qm);
if(points_wanted < QUERY_PLAN_MIN_POINTS)
// when selecting tiers, aim for a resolution of at least QUERY_PLAN_MIN_POINTS points
points_wanted = (before_wanted - after_wanted) > QUERY_PLAN_MIN_POINTS ? QUERY_PLAN_MIN_POINTS : before_wanted - after_wanted;
time_t min_first_time_s = 0;
time_t max_last_time_s = 0;
for(size_t tier = 0; tier < storage_tiers ; tier++) {
time_t first_time_s = qm->tiers[tier].db_first_time_s;
time_t last_time_s = qm->tiers[tier].db_last_time_s;
if(!min_first_time_s || (first_time_s && first_time_s < min_first_time_s))
min_first_time_s = first_time_s;
if(!max_last_time_s || (last_time_s && last_time_s > max_last_time_s))
max_last_time_s = last_time_s;
}
for(size_t tier = 0; tier < storage_tiers ; tier++) {
// find the db time-range for this tier for all metrics
STORAGE_METRIC_HANDLE *smh = qm->tiers[tier].smh;
time_t first_time_s = qm->tiers[tier].db_first_time_s;
time_t last_time_s = qm->tiers[tier].db_last_time_s;
time_t update_every_s = qm->tiers[tier].db_update_every_s;
if( !smh ||
!first_time_s ||
!last_time_s ||
!update_every_s ||
first_time_s > before_wanted ||
last_time_s < after_wanted
) {
qm->tiers[tier].weight = -LONG_MAX;
continue;
}
internal_fatal(first_time_s > before_wanted || last_time_s < after_wanted, "QUERY: invalid db durations");
qm->tiers[tier].weight = query_plan_points_coverage_weight(
min_first_time_s, max_last_time_s, update_every_s,
after_wanted, before_wanted, points_wanted, tier);
}
size_t best_tier = 0;
for(size_t tier = 1; tier < storage_tiers ; tier++) {
if(qm->tiers[tier].weight >= qm->tiers[best_tier].weight)
best_tier = tier;
}
return best_tier;
}
static size_t rrddim_find_best_tier_for_timeframe(QUERY_TARGET *qt, time_t after_wanted, time_t before_wanted, size_t points_wanted) {
if(unlikely(storage_tiers < 2))
return 0;
if(unlikely(after_wanted == before_wanted || points_wanted <= 0)) {
internal_error(true, "QUERY: '%s' has invalid params to tier calculation", qt->id);
return 0;
}
long weight[storage_tiers];
for(size_t tier = 0; tier < storage_tiers ; tier++) {
time_t common_first_time_s = 0;
time_t common_last_time_s = 0;
time_t common_update_every_s = 0;
// find the db time-range for this tier for all metrics
for(size_t i = 0, used = qt->query.used; i < used ; i++) {
QUERY_METRIC *qm = query_metric(qt, i);
time_t first_time_s = qm->tiers[tier].db_first_time_s;
time_t last_time_s = qm->tiers[tier].db_last_time_s;
time_t update_every_s = qm->tiers[tier].db_update_every_s;
if(!first_time_s || !last_time_s || !update_every_s)
continue;
if(!common_first_time_s)
common_first_time_s = first_time_s;
else
common_first_time_s = MIN(first_time_s, common_first_time_s);
if(!common_last_time_s)
common_last_time_s = last_time_s;
else
common_last_time_s = MAX(last_time_s, common_last_time_s);
if(!common_update_every_s)
common_update_every_s = update_every_s;
else
common_update_every_s = MIN(update_every_s, common_update_every_s);
}
weight[tier] = query_plan_points_coverage_weight(common_first_time_s, common_last_time_s, common_update_every_s, after_wanted, before_wanted, points_wanted, tier);
}
size_t best_tier = 0;
for(size_t tier = 1; tier < storage_tiers ; tier++) {
if(weight[tier] >= weight[best_tier])
best_tier = tier;
}
if(weight[best_tier] == -LONG_MAX)
best_tier = 0;
return best_tier;
}
static time_t rrdset_find_natural_update_every_for_timeframe(QUERY_TARGET *qt, time_t after_wanted, time_t before_wanted, size_t points_wanted, RRDR_OPTIONS options, size_t tier) {
size_t best_tier;
if((options & RRDR_OPTION_SELECTED_TIER) && tier < storage_tiers)
best_tier = tier;
else
best_tier = rrddim_find_best_tier_for_timeframe(qt, after_wanted, before_wanted, points_wanted);
// find the db minimum update every for this tier for all metrics
time_t common_update_every_s = default_rrd_update_every;
for(size_t i = 0, used = qt->query.used; i < used ; i++) {
QUERY_METRIC *qm = query_metric(qt, i);
time_t update_every_s = qm->tiers[best_tier].db_update_every_s;
if(!i)
common_update_every_s = update_every_s;
else
common_update_every_s = MIN(update_every_s, common_update_every_s);
}
return common_update_every_s;
}
// ----------------------------------------------------------------------------
// query ops
typedef struct query_point {
STORAGE_POINT sp;
NETDATA_DOUBLE value;
bool added;
#ifdef NETDATA_INTERNAL_CHECKS
size_t id;
#endif
} QUERY_POINT;
QUERY_POINT QUERY_POINT_EMPTY = {
.sp = STORAGE_POINT_UNSET,
.value = NAN,
.added = false,
#ifdef NETDATA_INTERNAL_CHECKS
.id = 0,
#endif
};
#ifdef NETDATA_INTERNAL_CHECKS
#define query_point_set_id(point, point_id) (point).id = point_id
#else
#define query_point_set_id(point, point_id) debug_dummy()
#endif
typedef struct query_engine_ops {
// configuration
RRDR *r;
QUERY_METRIC *qm;
time_t view_update_every;
time_t query_granularity;
TIER_QUERY_FETCH tier_query_fetch;
// query planer
size_t current_plan;
time_t current_plan_expire_time;
time_t plan_expanded_after;
time_t plan_expanded_before;
// storage queries
size_t tier;
struct query_metric_tier *tier_ptr;
struct storage_engine_query_handle *seqh;
// aggregating points over time
size_t group_points_non_zero;
size_t group_points_added;
STORAGE_POINT group_point; // aggregates min, max, sum, count, anomaly count for each group point
STORAGE_POINT query_point; // aggregates min, max, sum, count, anomaly count across the whole query
RRDR_VALUE_FLAGS group_value_flags;
// statistics
size_t db_total_points_read;
size_t db_points_read_per_tier[RRD_STORAGE_TIERS];
struct {
time_t expanded_after;
time_t expanded_before;
struct storage_engine_query_handle handle;
bool initialized;
bool finalized;
} plans[QUERY_PLANS_MAX];
struct query_engine_ops *next;
} QUERY_ENGINE_OPS;
// ----------------------------------------------------------------------------
// query planer
#define query_plan_should_switch_plan(ops, now) ((now) >= (ops)->current_plan_expire_time)
static size_t query_planer_expand_duration_in_points(time_t this_update_every, time_t next_update_every) {
time_t delta = this_update_every - next_update_every;
if(delta < 0) delta = -delta;
size_t points;
if(delta < this_update_every * POINTS_TO_EXPAND_QUERY)
points = POINTS_TO_EXPAND_QUERY;
else
points = (delta + this_update_every - 1) / this_update_every;
return points;
}
static void query_planer_initialize_plans(QUERY_ENGINE_OPS *ops) {
QUERY_METRIC *qm = ops->qm;
for(size_t p = 0; p < qm->plan.used ; p++) {
size_t tier = qm->plan.array[p].tier;
time_t update_every = qm->tiers[tier].db_update_every_s;
size_t points_to_add_to_after;
if(p > 0) {
// there is another plan before to this
size_t tier0 = qm->plan.array[p - 1].tier;
time_t update_every0 = qm->tiers[tier0].db_update_every_s;
points_to_add_to_after = query_planer_expand_duration_in_points(update_every, update_every0);
}
else
points_to_add_to_after = (tier == 0) ? 0 : POINTS_TO_EXPAND_QUERY;
size_t points_to_add_to_before;
if(p + 1 < qm->plan.used) {
// there is another plan after to this
size_t tier1 = qm->plan.array[p+1].tier;
time_t update_every1 = qm->tiers[tier1].db_update_every_s;
points_to_add_to_before = query_planer_expand_duration_in_points(update_every, update_every1);
}
else
points_to_add_to_before = POINTS_TO_EXPAND_QUERY;
time_t after = qm->plan.array[p].after - (time_t)(update_every * points_to_add_to_after);
time_t before = qm->plan.array[p].before + (time_t)(update_every * points_to_add_to_before);
ops->plans[p].expanded_after = after;
ops->plans[p].expanded_before = before;
ops->r->internal.qt->db.tiers[tier].queries++;
struct query_metric_tier *tier_ptr = &qm->tiers[tier];
STORAGE_ENGINE *eng = query_metric_storage_engine(ops->r->internal.qt, qm, tier);
storage_engine_query_init(eng->seb, tier_ptr->smh, &ops->plans[p].handle,
after, before, ops->r->internal.qt->request.priority);
ops->plans[p].initialized = true;
ops->plans[p].finalized = false;
}
}
static void query_planer_finalize_plan(QUERY_ENGINE_OPS *ops, size_t plan_id) {
// QUERY_METRIC *qm = ops->qm;
if(ops->plans[plan_id].initialized && !ops->plans[plan_id].finalized) {
storage_engine_query_finalize(&ops->plans[plan_id].handle);
ops->plans[plan_id].initialized = false;
ops->plans[plan_id].finalized = true;
}
}
static void query_planer_finalize_remaining_plans(QUERY_ENGINE_OPS *ops) {
QUERY_METRIC *qm = ops->qm;
for(size_t p = 0; p < qm->plan.used ; p++)
query_planer_finalize_plan(ops, p);
}
static void query_planer_activate_plan(QUERY_ENGINE_OPS *ops, size_t plan_id, time_t overwrite_after __maybe_unused) {
QUERY_METRIC *qm = ops->qm;
internal_fatal(plan_id >= qm->plan.used, "QUERY: invalid plan_id given");
internal_fatal(!ops->plans[plan_id].initialized, "QUERY: plan has not been initialized");
internal_fatal(ops->plans[plan_id].finalized, "QUERY: plan has been finalized");
internal_fatal(qm->plan.array[plan_id].after > qm->plan.array[plan_id].before, "QUERY: flipped after/before");
ops->tier = qm->plan.array[plan_id].tier;
ops->tier_ptr = &qm->tiers[ops->tier];
ops->seqh = &ops->plans[plan_id].handle;
ops->current_plan = plan_id;
if(plan_id + 1 < qm->plan.used && qm->plan.array[plan_id + 1].after < qm->plan.array[plan_id].before)
ops->current_plan_expire_time = qm->plan.array[plan_id + 1].after;
else
ops->current_plan_expire_time = qm->plan.array[plan_id].before;
ops->plan_expanded_after = ops->plans[plan_id].expanded_after;
ops->plan_expanded_before = ops->plans[plan_id].expanded_before;
}
static bool query_planer_next_plan(QUERY_ENGINE_OPS *ops, time_t now, time_t last_point_end_time) {
QUERY_METRIC *qm = ops->qm;
size_t old_plan = ops->current_plan;
time_t next_plan_before_time;
do {
ops->current_plan++;
if (ops->current_plan >= qm->plan.used) {
ops->current_plan = old_plan;
ops->current_plan_expire_time = ops->r->internal.qt->window.before;
// let the query run with current plan
// we will not switch it
return false;
}
next_plan_before_time = qm->plan.array[ops->current_plan].before;
} while(now >= next_plan_before_time || last_point_end_time >= next_plan_before_time);
if(!query_metric_is_valid_tier(qm, qm->plan.array[ops->current_plan].tier)) {
ops->current_plan = old_plan;
ops->current_plan_expire_time = ops->r->internal.qt->window.before;
return false;
}
query_planer_finalize_plan(ops, old_plan);
query_planer_activate_plan(ops, ops->current_plan, MIN(now, last_point_end_time));
return true;
}
static int compare_query_plan_entries_on_start_time(const void *a, const void *b) {
QUERY_PLAN_ENTRY *p1 = (QUERY_PLAN_ENTRY *)a;
QUERY_PLAN_ENTRY *p2 = (QUERY_PLAN_ENTRY *)b;
return (p1->after < p2->after)?-1:1;
}
static bool query_plan(QUERY_ENGINE_OPS *ops, time_t after_wanted, time_t before_wanted, size_t points_wanted) {
QUERY_METRIC *qm = ops->qm;
// put our selected tier as the first plan
size_t selected_tier;
bool switch_tiers = true;
if((ops->r->internal.qt->window.options & RRDR_OPTION_SELECTED_TIER)
&& ops->r->internal.qt->window.tier < storage_tiers
&& query_metric_is_valid_tier(qm, ops->r->internal.qt->window.tier)) {
selected_tier = ops->r->internal.qt->window.tier;
switch_tiers = false;
}
else {
selected_tier = query_metric_best_tier_for_timeframe(qm, after_wanted, before_wanted, points_wanted);
if(!query_metric_is_valid_tier(qm, selected_tier))
return false;
}
if(qm->tiers[selected_tier].db_first_time_s > before_wanted ||
qm->tiers[selected_tier].db_last_time_s < after_wanted) {
// we don't have any data to satisfy this query
return false;
}
qm->plan.used = 1;
qm->plan.array[0].tier = selected_tier;
qm->plan.array[0].after = (qm->tiers[selected_tier].db_first_time_s < after_wanted) ? after_wanted : qm->tiers[selected_tier].db_first_time_s;
qm->plan.array[0].before = (qm->tiers[selected_tier].db_last_time_s > before_wanted) ? before_wanted : qm->tiers[selected_tier].db_last_time_s;
if(switch_tiers) {
// the selected tier
time_t selected_tier_first_time_s = qm->plan.array[0].after;
time_t selected_tier_last_time_s = qm->plan.array[0].before;
// check if our selected tier can start the query
if (selected_tier_first_time_s > after_wanted) {
// we need some help from other tiers
for (size_t tr = (int)selected_tier + 1; tr < storage_tiers && qm->plan.used < QUERY_PLANS_MAX ; tr++) {
if(!query_metric_is_valid_tier(qm, tr))
continue;
// find the first time of this tier
time_t tier_first_time_s = qm->tiers[tr].db_first_time_s;
time_t tier_last_time_s = qm->tiers[tr].db_last_time_s;
// can it help?
if (tier_first_time_s < selected_tier_first_time_s && tier_first_time_s <= before_wanted && tier_last_time_s >= after_wanted) {
// it can help us add detail at the beginning of the query
QUERY_PLAN_ENTRY t = {
.tier = tr,
.after = (tier_first_time_s < after_wanted) ? after_wanted : tier_first_time_s,
.before = selected_tier_first_time_s,
};
ops->plans[qm->plan.used].initialized = false;
ops->plans[qm->plan.used].finalized = false;
qm->plan.array[qm->plan.used++] = t;
internal_fatal(!t.after || !t.before, "QUERY: invalid plan selected");
// prepare for the tier
selected_tier_first_time_s = t.after;
if (t.after <= after_wanted)
break;
}
}
}
// check if our selected tier can finish the query
if (selected_tier_last_time_s < before_wanted) {
// we need some help from other tiers
for (int tr = (int)selected_tier - 1; tr >= 0 && qm->plan.used < QUERY_PLANS_MAX ; tr--) {
if(!query_metric_is_valid_tier(qm, tr))
continue;
// find the last time of this tier
time_t tier_first_time_s = qm->tiers[tr].db_first_time_s;
time_t tier_last_time_s = qm->tiers[tr].db_last_time_s;
//buffer_sprintf(wb, ": EVAL BEFORE tier %d, %ld", tier, last_time_s);
// can it help?
if (tier_last_time_s > selected_tier_last_time_s && tier_first_time_s <= before_wanted && tier_last_time_s >= after_wanted) {
// it can help us add detail at the end of the query
QUERY_PLAN_ENTRY t = {
.tier = tr,
.after = selected_tier_last_time_s,
.before = (tier_last_time_s > before_wanted) ? before_wanted : tier_last_time_s,
};
ops->plans[qm->plan.used].initialized = false;
ops->plans[qm->plan.used].finalized = false;
qm->plan.array[qm->plan.used++] = t;
// prepare for the tier
selected_tier_last_time_s = t.before;
internal_fatal(!t.after || !t.before, "QUERY: invalid plan selected");
if (t.before >= before_wanted)
break;
}
}
}
}
// sort the query plan
if(qm->plan.used > 1)
qsort(&qm->plan.array, qm->plan.used, sizeof(QUERY_PLAN_ENTRY), compare_query_plan_entries_on_start_time);
if(!query_metric_is_valid_tier(qm, qm->plan.array[0].tier))
return false;
#ifdef NETDATA_INTERNAL_CHECKS
for(size_t p = 0; p < qm->plan.used ;p++) {
internal_fatal(qm->plan.array[p].after > qm->plan.array[p].before, "QUERY: flipped after/before");
internal_fatal(qm->plan.array[p].after < after_wanted, "QUERY: too small plan first time");
internal_fatal(qm->plan.array[p].before > before_wanted, "QUERY: too big plan last time");
}
#endif
query_planer_initialize_plans(ops);
query_planer_activate_plan(ops, 0, 0);
return true;
}
// ----------------------------------------------------------------------------
// dimension level query engine
#define query_interpolate_point(this_point, last_point, now) do { \
if(likely( \
/* the point to interpolate is more than 1s wide */ \
(this_point).sp.end_time_s - (this_point).sp.start_time_s > 1 \
\
/* the two points are exactly next to each other */ \
&& (last_point).sp.end_time_s == (this_point).sp.start_time_s \
\
/* both points are valid numbers */ \
&& netdata_double_isnumber((this_point).value) \
&& netdata_double_isnumber((last_point).value) \
\
)) { \
(this_point).value = (last_point).value + ((this_point).value - (last_point).value) * (1.0 - (NETDATA_DOUBLE)((this_point).sp.end_time_s - (now)) / (NETDATA_DOUBLE)((this_point).sp.end_time_s - (this_point).sp.start_time_s)); \
(this_point).sp.end_time_s = now; \
} \
} while(0)
#define query_add_point_to_group(r, point, ops, add_flush) do { \
if(likely(netdata_double_isnumber((point).value))) { \
if(likely(fpclassify((point).value) != FP_ZERO)) \
(ops)->group_points_non_zero++; \
\
if(unlikely((point).sp.flags & SN_FLAG_RESET)) \
(ops)->group_value_flags |= RRDR_VALUE_RESET; \
\
time_grouping_add(r, (point).value, add_flush); \
\
storage_point_merge_to((ops)->group_point, (point).sp); \
if(!(point).added) \
storage_point_merge_to((ops)->query_point, (point).sp); \
} \
\
(ops)->group_points_added++; \
} while(0)
static __thread QUERY_ENGINE_OPS *released_ops = NULL;
static void rrd2rrdr_query_ops_freeall(RRDR *r __maybe_unused) {
while(released_ops) {
QUERY_ENGINE_OPS *ops = released_ops;
released_ops = ops->next;
onewayalloc_freez(r->internal.owa, ops);
}
}
static void rrd2rrdr_query_ops_release(QUERY_ENGINE_OPS *ops) {
if(!ops) return;
ops->next = released_ops;
released_ops = ops;
}
static QUERY_ENGINE_OPS *rrd2rrdr_query_ops_get(RRDR *r) {
QUERY_ENGINE_OPS *ops;
if(released_ops) {
ops = released_ops;
released_ops = ops->next;
}
else {
ops = onewayalloc_mallocz(r->internal.owa, sizeof(QUERY_ENGINE_OPS));
}
memset(ops, 0, sizeof(*ops));
return ops;
}
static QUERY_ENGINE_OPS *rrd2rrdr_query_ops_prep(RRDR *r, size_t query_metric_id) {
QUERY_TARGET *qt = r->internal.qt;
QUERY_ENGINE_OPS *ops = rrd2rrdr_query_ops_get(r);
*ops = (QUERY_ENGINE_OPS) {
.r = r,
.qm = query_metric(qt, query_metric_id),
.tier_query_fetch = r->time_grouping.tier_query_fetch,
.view_update_every = r->view.update_every,
.query_granularity = (time_t)(r->view.update_every / r->view.group),
.group_value_flags = RRDR_VALUE_NOTHING,
};
if(!query_plan(ops, qt->window.after, qt->window.before, qt->window.points)) {
rrd2rrdr_query_ops_release(ops);
return NULL;
}
return ops;
}
static void rrd2rrdr_query_execute(RRDR *r, size_t dim_id_in_rrdr, QUERY_ENGINE_OPS *ops) {
QUERY_TARGET *qt = r->internal.qt;
QUERY_METRIC *qm = ops->qm;
const RRDR_TIME_GROUPING add_flush = r->time_grouping.add_flush;
ops->group_point = STORAGE_POINT_UNSET;
ops->query_point = STORAGE_POINT_UNSET;
RRDR_OPTIONS options = qt->window.options;
size_t points_wanted = qt->window.points;
time_t after_wanted = qt->window.after;
time_t before_wanted = qt->window.before; (void)before_wanted;
// bool debug_this = false;
// if(strcmp("user", string2str(rd->id)) == 0 && strcmp("system.cpu", string2str(rd->rrdset->id)) == 0)
// debug_this = true;
size_t points_added = 0;
long rrdr_line = -1;
bool use_anomaly_bit_as_value = (r->internal.qt->window.options & RRDR_OPTION_ANOMALY_BIT) ? true : false;
NETDATA_DOUBLE min = r->view.min, max = r->view.max;
QUERY_POINT last2_point = QUERY_POINT_EMPTY;
QUERY_POINT last1_point = QUERY_POINT_EMPTY;
QUERY_POINT new_point = QUERY_POINT_EMPTY;
// ONE POINT READ-AHEAD
// when we switch plans, we read-ahead a point from the next plan
// to join them smoothly at the exact time the next plan begins
STORAGE_POINT next1_point = STORAGE_POINT_UNSET;
time_t now_start_time = after_wanted - ops->query_granularity;
time_t now_end_time = after_wanted + ops->view_update_every - ops->query_granularity;
size_t db_points_read_since_plan_switch = 0; (void)db_points_read_since_plan_switch;
size_t query_is_finished_counter = 0;
// The main loop, based on the query granularity we need
for( ; points_added < points_wanted && query_is_finished_counter <= 10 ;
now_start_time = now_end_time, now_end_time += ops->view_update_every) {
if(unlikely(query_plan_should_switch_plan(ops, now_end_time))) {
query_planer_next_plan(ops, now_end_time, new_point.sp.end_time_s);
db_points_read_since_plan_switch = 0;
}
// read all the points of the db, prior to the time we need (now_end_time)
size_t count_same_end_time = 0;
while(count_same_end_time < 100) {
if(likely(count_same_end_time == 0)) {
last2_point = last1_point;
last1_point = new_point;
}
if(unlikely(storage_engine_query_is_finished(ops->seqh))) {
query_is_finished_counter++;
if(count_same_end_time != 0) {
last2_point = last1_point;
last1_point = new_point;
}
new_point = QUERY_POINT_EMPTY;
new_point.sp.start_time_s = last1_point.sp.end_time_s;
new_point.sp.end_time_s = now_end_time;
//
// if(debug_this) netdata_log_info("QUERY: is finished() returned true");
//
break;
}
else
query_is_finished_counter = 0;
// fetch the new point
{
STORAGE_POINT sp;
if(likely(storage_point_is_unset(next1_point))) {
db_points_read_since_plan_switch++;
sp = storage_engine_query_next_metric(ops->seqh);
ops->db_points_read_per_tier[ops->tier]++;
ops->db_total_points_read++;
if(unlikely(options & RRDR_OPTION_ABSOLUTE))
storage_point_make_positive(sp);
}
else {
// ONE POINT READ-AHEAD
sp = next1_point;
storage_point_unset(next1_point);
db_points_read_since_plan_switch = 1;
}
// ONE POINT READ-AHEAD
if(unlikely(query_plan_should_switch_plan(ops, sp.end_time_s) &&
query_planer_next_plan(ops, now_end_time, new_point.sp.end_time_s))) {
// The end time of the current point, crosses our plans (tiers)
// so, we switched plan (tier)
//
// There are 2 cases now:
//
// A. the entire point of the previous plan is to the future of point from the next plan
// B. part of the point of the previous plan overlaps with the point from the next plan
STORAGE_POINT sp2 = storage_engine_query_next_metric(ops->seqh);
ops->db_points_read_per_tier[ops->tier]++;
ops->db_total_points_read++;
if(unlikely(options & RRDR_OPTION_ABSOLUTE))
storage_point_make_positive(sp);
if(sp.start_time_s > sp2.start_time_s)
// the point from the previous plan is useless
sp = sp2;
else
// let the query run from the previous plan
// but setting this will also cut off the interpolation
// of the point from the previous plan
next1_point = sp2;
}
new_point.sp = sp;
new_point.added = false;
query_point_set_id(new_point, ops->db_total_points_read);
// if(debug_this)
// netdata_log_info("QUERY: got point %zu, from time %ld to %ld // now from %ld to %ld // query from %ld to %ld",
// new_point.id, new_point.start_time, new_point.end_time, now_start_time, now_end_time, after_wanted, before_wanted);
//
// get the right value from the point we got
if(likely(!storage_point_is_unset(sp) && !storage_point_is_gap(sp))) {
if(unlikely(use_anomaly_bit_as_value))
new_point.value = storage_point_anomaly_rate(new_point.sp);
else {
switch (ops->tier_query_fetch) {
default:
case TIER_QUERY_FETCH_AVERAGE:
new_point.value = sp.sum / (NETDATA_DOUBLE)sp.count;
break;
case TIER_QUERY_FETCH_MIN:
new_point.value = sp.min;
break;
case TIER_QUERY_FETCH_MAX:
new_point.value = sp.max;
break;
case TIER_QUERY_FETCH_SUM:
new_point.value = sp.sum;
break;
}
}
}
else
new_point.value = NAN;
}
// check if the db is giving us zero duration points
if(unlikely(db_points_read_since_plan_switch > 1 &&
new_point.sp.start_time_s == new_point.sp.end_time_s)) {
internal_error(true, "QUERY: '%s', dimension '%s' next_metric() returned "
"point %zu from %ld to %ld, that are both equal",
qt->id, query_metric_id(qt, qm),
new_point.id, new_point.sp.start_time_s, new_point.sp.end_time_s);
new_point.sp.start_time_s = new_point.sp.end_time_s - ops->tier_ptr->db_update_every_s;
}
// check if the db is advancing the query
if(unlikely(db_points_read_since_plan_switch > 1 &&
new_point.sp.end_time_s <= last1_point.sp.end_time_s)) {
internal_error(true,
"QUERY: '%s', dimension '%s' next_metric() returned "
"point %zu from %ld to %ld, before the "
"last point %zu from %ld to %ld, "
"now is %ld to %ld",
qt->id, query_metric_id(qt, qm),
new_point.id, new_point.sp.start_time_s, new_point.sp.end_time_s,
last1_point.id, last1_point.sp.start_time_s, last1_point.sp.end_time_s,
now_start_time, now_end_time);
count_same_end_time++;
continue;
}
count_same_end_time = 0;
// decide how to use this point
if(likely(new_point.sp.end_time_s < now_end_time)) { // likely to favor tier0
// this db point ends before our now_end_time
if(likely(new_point.sp.end_time_s >= now_start_time)) { // likely to favor tier0
// this db point ends after our now_start time
query_add_point_to_group(r, new_point, ops, add_flush);
new_point.added = true;
}
else {
// we don't need this db point
// it is totally outside our current time-frame
// this is desirable for the first point of the query
// because it allows us to interpolate the next point
// at exactly the time we will want
// we only log if this is not point 1
internal_error(new_point.sp.end_time_s < ops->plan_expanded_after &&
db_points_read_since_plan_switch > 1,
"QUERY: '%s', dimension '%s' next_metric() "
"returned point %zu from %ld time %ld, "
"which is entirely before our current timeframe %ld to %ld "
"(and before the entire query, after %ld, before %ld)",
qt->id, query_metric_id(qt, qm),
new_point.id, new_point.sp.start_time_s, new_point.sp.end_time_s,
now_start_time, now_end_time,
ops->plan_expanded_after, ops->plan_expanded_before);
}
}
else {
// the point ends in the future
// so, we will interpolate it below, at the inner loop
break;
}
}
if(unlikely(count_same_end_time)) {
internal_error(true,
"QUERY: '%s', dimension '%s', the database does not advance the query,"
" it returned an end time less or equal to the end time of the last "
"point we got %ld, %zu times",
qt->id, query_metric_id(qt, qm),
last1_point.sp.end_time_s, count_same_end_time);
if(unlikely(new_point.sp.end_time_s <= last1_point.sp.end_time_s))
new_point.sp.end_time_s = now_end_time;
}
time_t stop_time = new_point.sp.end_time_s;
if(unlikely(!storage_point_is_unset(next1_point) && next1_point.start_time_s >= now_end_time)) {
// ONE POINT READ-AHEAD
// the point crosses the start time of the
// read ahead storage point we have read
stop_time = next1_point.start_time_s;
}
// the inner loop
// we have 3 points in memory: last2, last1, new
// we select the one to use based on their timestamps
internal_fatal(now_end_time > stop_time || points_added >= points_wanted,
"QUERY: first part of query provides invalid point to interpolate (now_end_time %ld, stop_time %ld",
now_end_time, stop_time);
do {
// now_start_time is wrong in this loop
// but, we don't need it
QUERY_POINT current_point;
if(likely(now_end_time > new_point.sp.start_time_s)) {
// it is time for our NEW point to be used
current_point = new_point;
new_point.added = true; // first copy, then set it, so that new_point will not be added again
query_interpolate_point(current_point, last1_point, now_end_time);
// internal_error(current_point.id > 0
// && last1_point.id == 0
// && current_point.end_time > after_wanted
// && current_point.end_time > now_end_time,
// "QUERY: '%s', dimension '%s', after %ld, before %ld, view update every %ld,"
// " query granularity %ld, interpolating point %zu (from %ld to %ld) at %ld,"
// " but we could really favor by having last_point1 in this query.",
// qt->id, string2str(qm->dimension.id),
// after_wanted, before_wanted,
// ops.view_update_every, ops.query_granularity,
// current_point.id, current_point.start_time, current_point.end_time,
// now_end_time);
}
else if(likely(now_end_time <= last1_point.sp.end_time_s)) {
// our LAST point is still valid
current_point = last1_point;
last1_point.added = true; // first copy, then set it, so that last1_point will not be added again
query_interpolate_point(current_point, last2_point, now_end_time);
// internal_error(current_point.id > 0
// && last2_point.id == 0
// && current_point.end_time > after_wanted
// && current_point.end_time > now_end_time,
// "QUERY: '%s', dimension '%s', after %ld, before %ld, view update every %ld,"
// " query granularity %ld, interpolating point %zu (from %ld to %ld) at %ld,"
// " but we could really favor by having last_point2 in this query.",
// qt->id, string2str(qm->dimension.id),
// after_wanted, before_wanted, ops.view_update_every, ops.query_granularity,
// current_point.id, current_point.start_time, current_point.end_time,
// now_end_time);
}
else {
// a GAP, we don't have a value this time
current_point = QUERY_POINT_EMPTY;
}
query_add_point_to_group(r, current_point, ops, add_flush);
rrdr_line = rrdr_line_init(r, now_end_time, rrdr_line);
size_t rrdr_o_v_index = rrdr_line * r->d + dim_id_in_rrdr;
// find the place to store our values
RRDR_VALUE_FLAGS *rrdr_value_options_ptr = &r->o[rrdr_o_v_index];
// update the dimension options
if(likely(ops->group_points_non_zero))
r->od[dim_id_in_rrdr] |= RRDR_DIMENSION_NONZERO;
// store the specific point options
*rrdr_value_options_ptr = ops->group_value_flags;
// store the group value
NETDATA_DOUBLE group_value = time_grouping_flush(r, rrdr_value_options_ptr, add_flush);
r->v[rrdr_o_v_index] = group_value;
r->ar[rrdr_o_v_index] = storage_point_anomaly_rate(ops->group_point);
if(likely(points_added || r->internal.queries_count)) {
// find the min/max across all dimensions
if(unlikely(group_value < min)) min = group_value;
if(unlikely(group_value > max)) max = group_value;
}
else {
// runs only when r->internal.queries_count == 0 && points_added == 0
// so, on the first point added for the query.
min = max = group_value;
}
points_added++;
ops->group_points_added = 0;
ops->group_value_flags = RRDR_VALUE_NOTHING;
ops->group_points_non_zero = 0;
ops->group_point = STORAGE_POINT_UNSET;
now_end_time += ops->view_update_every;
} while(now_end_time <= stop_time && points_added < points_wanted);
// the loop above increased "now" by ops->view_update_every,
// but the main loop will increase it too,
// so, let's undo the last iteration of this loop
now_end_time -= ops->view_update_every;
}
query_planer_finalize_remaining_plans(ops);
qm->query_points = ops->query_point;
// fill the rest of the points with empty values
while (points_added < points_wanted) {
rrdr_line++;
size_t rrdr_o_v_index = rrdr_line * r->d + dim_id_in_rrdr;
r->o[rrdr_o_v_index] = RRDR_VALUE_EMPTY;
r->v[rrdr_o_v_index] = 0.0;
r->ar[rrdr_o_v_index] = 0.0;
points_added++;
}
r->internal.queries_count++;
r->view.min = min;
r->view.max = max;
r->stats.result_points_generated += points_added;
r->stats.db_points_read += ops->db_total_points_read;
for(size_t tr = 0; tr < storage_tiers ; tr++)
qt->db.tiers[tr].points += ops->db_points_read_per_tier[tr];
}
// ----------------------------------------------------------------------------
// fill the gap of a tier
void store_metric_at_tier(RRDDIM *rd, size_t tier, struct rrddim_tier *t, STORAGE_POINT sp, usec_t now_ut);
void rrdr_fill_tier_gap_from_smaller_tiers(RRDDIM *rd, size_t tier, time_t now_s) {
if(unlikely(tier >= storage_tiers)) return;
#ifdef ENABLE_DBENGINE
if(default_backfill == RRD_BACKFILL_NONE) return;
#else
return;
#endif
struct rrddim_tier *t = &rd->tiers[tier];
if(unlikely(!t)) return;
time_t latest_time_s = storage_engine_latest_time_s(t->seb, t->smh);
time_t granularity = (time_t)t->tier_grouping * (time_t)rd->rrdset->update_every;
time_t time_diff = now_s - latest_time_s;
// if the user wants only NEW backfilling, and we don't have any data
#ifdef ENABLE_DBENGINE
if(default_backfill == RRD_BACKFILL_NEW && latest_time_s <= 0) return;
#else
return;
#endif
// there is really nothing we can do
if(now_s <= latest_time_s || time_diff < granularity) return;
struct storage_engine_query_handle seqh;
// for each lower tier
for(int read_tier = (int)tier - 1; read_tier >= 0 ; read_tier--){
time_t smaller_tier_first_time = storage_engine_oldest_time_s(rd->tiers[read_tier].seb, rd->tiers[read_tier].smh);
time_t smaller_tier_last_time = storage_engine_latest_time_s(rd->tiers[read_tier].seb, rd->tiers[read_tier].smh);
if(smaller_tier_last_time <= latest_time_s) continue; // it is as bad as we are
long after_wanted = (latest_time_s < smaller_tier_first_time) ? smaller_tier_first_time : latest_time_s;
long before_wanted = smaller_tier_last_time;
struct rrddim_tier *tmp = &rd->tiers[read_tier];
storage_engine_query_init(tmp->seb, tmp->smh, &seqh, after_wanted, before_wanted, STORAGE_PRIORITY_HIGH);
size_t points_read = 0;
while(!storage_engine_query_is_finished(&seqh)) {
STORAGE_POINT sp = storage_engine_query_next_metric(&seqh);
points_read++;
if(sp.end_time_s > latest_time_s) {
latest_time_s = sp.end_time_s;
store_metric_at_tier(rd, tier, t, sp, sp.end_time_s * USEC_PER_SEC);
}
}
storage_engine_query_finalize(&seqh);
store_metric_collection_completed();
global_statistics_backfill_query_completed(points_read);
//internal_error(true, "DBENGINE: backfilled chart '%s', dimension '%s', tier %d, from %ld to %ld, with %zu points from tier %d",
// rd->rrdset->name, rd->name, tier, after_wanted, before_wanted, points, tr);
}
}
// ----------------------------------------------------------------------------
// fill RRDR for the whole chart
#ifdef NETDATA_INTERNAL_CHECKS
static void rrd2rrdr_log_request_response_metadata(RRDR *r
, RRDR_OPTIONS options __maybe_unused
, RRDR_TIME_GROUPING group_method
, bool aligned
, size_t group
, time_t resampling_time
, size_t resampling_group
, time_t after_wanted
, time_t after_requested
, time_t before_wanted
, time_t before_requested
, size_t points_requested
, size_t points_wanted
//, size_t after_slot
//, size_t before_slot
, const char *msg
) {
QUERY_TARGET *qt = r->internal.qt;
time_t first_entry_s = qt->db.first_time_s;
time_t last_entry_s = qt->db.last_time_s;
internal_error(
true,
"rrd2rrdr() on %s update every %ld with %s grouping %s (group: %zu, resampling_time: %ld, resampling_group: %zu), "
"after (got: %ld, want: %ld, req: %ld, db: %ld), "
"before (got: %ld, want: %ld, req: %ld, db: %ld), "
"duration (got: %ld, want: %ld, req: %ld, db: %ld), "
"points (got: %zu, want: %zu, req: %zu), "
"%s"
, qt->id
, qt->window.query_granularity
// grouping
, (aligned) ? "aligned" : "unaligned"
, time_grouping_id2txt(group_method)
, group
, resampling_time
, resampling_group
// after
, r->view.after
, after_wanted
, after_requested
, first_entry_s
// before
, r->view.before
, before_wanted
, before_requested
, last_entry_s
// duration
, (long)(r->view.before - r->view.after + qt->window.query_granularity)
, (long)(before_wanted - after_wanted + qt->window.query_granularity)
, (long)before_requested - after_requested
, (long)((last_entry_s - first_entry_s) + qt->window.query_granularity)
// points
, r->rows
, points_wanted
, points_requested
// message
, msg
);
}
#endif // NETDATA_INTERNAL_CHECKS
// #define DEBUG_QUERY_LOGIC 1
#ifdef DEBUG_QUERY_LOGIC
#define query_debug_log_init() BUFFER *debug_log = buffer_create(1000)
#define query_debug_log(args...) buffer_sprintf(debug_log, ##args)
#define query_debug_log_fin() { \
netdata_log_info("QUERY: '%s', after:%ld, before:%ld, duration:%ld, points:%zu, res:%ld - wanted => after:%ld, before:%ld, points:%zu, group:%zu, granularity:%ld, resgroup:%ld, resdiv:" NETDATA_DOUBLE_FORMAT_AUTO " %s", qt->id, after_requested, before_requested, before_requested - after_requested, points_requested, resampling_time_requested, after_wanted, before_wanted, points_wanted, group, query_granularity, resampling_group, resampling_divisor, buffer_tostring(debug_log)); \
buffer_free(debug_log); \
debug_log = NULL; \
}
#define query_debug_log_free() do { buffer_free(debug_log); } while(0)
#else
#define query_debug_log_init() debug_dummy()
#define query_debug_log(args...) debug_dummy()
#define query_debug_log_fin() debug_dummy()
#define query_debug_log_free() debug_dummy()
#endif
bool query_target_calculate_window(QUERY_TARGET *qt) {
if (unlikely(!qt)) return false;
size_t points_requested = (long)qt->request.points;
time_t after_requested = qt->request.after;
time_t before_requested = qt->request.before;
RRDR_TIME_GROUPING group_method = qt->request.time_group_method;
time_t resampling_time_requested = qt->request.resampling_time;
RRDR_OPTIONS options = qt->window.options;
size_t tier = qt->request.tier;
time_t update_every = qt->db.minimum_latest_update_every_s ? qt->db.minimum_latest_update_every_s : 1;
// RULES
// points_requested = 0
// the user wants all the natural points the database has
//
// after_requested = 0
// the user wants to start the query from the oldest point in our database
//
// before_requested = 0
// the user wants the query to end to the latest point in our database
//
// when natural points are wanted, the query has to be aligned to the update_every
// of the database
size_t points_wanted = points_requested;
time_t after_wanted = after_requested;
time_t before_wanted = before_requested;
bool aligned = !(options & RRDR_OPTION_NOT_ALIGNED);
bool automatic_natural_points = (points_wanted == 0);
bool relative_period_requested = false;
bool natural_points = (options & RRDR_OPTION_NATURAL_POINTS) || automatic_natural_points;
bool before_is_aligned_to_db_end = false;
query_debug_log_init();
if (ABS(before_requested) <= API_RELATIVE_TIME_MAX || ABS(after_requested) <= API_RELATIVE_TIME_MAX) {
relative_period_requested = true;
natural_points = true;
options |= RRDR_OPTION_NATURAL_POINTS;
query_debug_log(":relative+natural");
}
// if the user wants virtual points, make sure we do it
if (options & RRDR_OPTION_VIRTUAL_POINTS)
natural_points = false;
// set the right flag about natural and virtual points
if (natural_points) {
options |= RRDR_OPTION_NATURAL_POINTS;
if (options & RRDR_OPTION_VIRTUAL_POINTS)
options &= ~RRDR_OPTION_VIRTUAL_POINTS;
}
else {
options |= RRDR_OPTION_VIRTUAL_POINTS;
if (options & RRDR_OPTION_NATURAL_POINTS)
options &= ~RRDR_OPTION_NATURAL_POINTS;
}
if (after_wanted == 0 || before_wanted == 0) {
relative_period_requested = true;
time_t first_entry_s = qt->db.first_time_s;
time_t last_entry_s = qt->db.last_time_s;
if (first_entry_s == 0 || last_entry_s == 0) {
internal_error(true, "QUERY: no data detected on query '%s' (db first_entry_t = %ld, last_entry_t = %ld)", qt->id, first_entry_s, last_entry_s);
after_wanted = qt->window.after;
before_wanted = qt->window.before;
if(after_wanted == before_wanted)
after_wanted = before_wanted - update_every;
if (points_wanted == 0) {
points_wanted = (before_wanted - after_wanted) / update_every;
query_debug_log(":zero points_wanted %zu", points_wanted);
}
}
else {
query_debug_log(":first_entry_t %ld, last_entry_t %ld", first_entry_s, last_entry_s);
if (after_wanted == 0) {
after_wanted = first_entry_s;
query_debug_log(":zero after_wanted %ld", after_wanted);
}
if (before_wanted == 0) {
before_wanted = last_entry_s;
before_is_aligned_to_db_end = true;
query_debug_log(":zero before_wanted %ld", before_wanted);
}
if (points_wanted == 0) {
points_wanted = (last_entry_s - first_entry_s) / update_every;
query_debug_log(":zero points_wanted %zu", points_wanted);
}
}
}
if (points_wanted == 0) {
points_wanted = 600;
query_debug_log(":zero600 points_wanted %zu", points_wanted);
}
// convert our before_wanted and after_wanted to absolute
rrdr_relative_window_to_absolute_query(&after_wanted, &before_wanted, NULL, unittest_running);
query_debug_log(":relative2absolute after %ld, before %ld", after_wanted, before_wanted);
if (natural_points && (options & RRDR_OPTION_SELECTED_TIER) && tier > 0 && storage_tiers > 1) {
update_every = rrdset_find_natural_update_every_for_timeframe(
qt, after_wanted, before_wanted, points_wanted, options, tier);
if (update_every <= 0) update_every = qt->db.minimum_latest_update_every_s;
query_debug_log(":natural update every %ld", update_every);
}
// this is the update_every of the query
// it may be different to the update_every of the database
time_t query_granularity = (natural_points) ? update_every : 1;
if (query_granularity <= 0) query_granularity = 1;
query_debug_log(":query_granularity %ld", query_granularity);
// align before_wanted and after_wanted to query_granularity
if (before_wanted % query_granularity) {
before_wanted -= before_wanted % query_granularity;
query_debug_log(":granularity align before_wanted %ld", before_wanted);
}
if (after_wanted % query_granularity) {
after_wanted -= after_wanted % query_granularity;
query_debug_log(":granularity align after_wanted %ld", after_wanted);
}
// automatic_natural_points is set when the user wants all the points available in the database
if (automatic_natural_points) {
points_wanted = (before_wanted - after_wanted + 1) / query_granularity;
if (unlikely(points_wanted <= 0)) points_wanted = 1;
query_debug_log(":auto natural points_wanted %zu", points_wanted);
}
time_t duration = before_wanted - after_wanted;
// if the resampling time is too big, extend the duration to the past
if (unlikely(resampling_time_requested > duration)) {
after_wanted = before_wanted - resampling_time_requested;
duration = before_wanted - after_wanted;
query_debug_log(":resampling after_wanted %ld", after_wanted);
}
// if the duration is not aligned to resampling time
// extend the duration to the past, to avoid a gap at the chart
// only when the missing duration is above 1/10th of a point
if (resampling_time_requested > query_granularity && duration % resampling_time_requested) {
time_t delta = duration % resampling_time_requested;
if (delta > resampling_time_requested / 10) {
after_wanted -= resampling_time_requested - delta;
duration = before_wanted - after_wanted;
query_debug_log(":resampling2 after_wanted %ld", after_wanted);
}
}
// the available points of the query
size_t points_available = (duration + 1) / query_granularity;
if (unlikely(points_available <= 0)) points_available = 1;
query_debug_log(":points_available %zu", points_available);
if (points_wanted > points_available) {
points_wanted = points_available;
query_debug_log(":max points_wanted %zu", points_wanted);
}
if(points_wanted > 86400 && !unittest_running) {
points_wanted = 86400;
query_debug_log(":absolute max points_wanted %zu", points_wanted);
}
// calculate the desired grouping of source data points
size_t group = points_available / points_wanted;
if (group == 0) group = 1;
// round "group" to the closest integer
if (points_available % points_wanted > points_wanted / 2)
group++;
query_debug_log(":group %zu", group);
if (points_wanted * group * query_granularity < (size_t)duration) {
// the grouping we are going to do, is not enough
// to cover the entire duration requested, so
// we have to change the number of points, to make sure we will
// respect the timeframe as closely as possibly
// let's see how many points are the optimal
points_wanted = points_available / group;
if (points_wanted * group < points_available)
points_wanted++;
if (unlikely(points_wanted == 0))
points_wanted = 1;
query_debug_log(":optimal points %zu", points_wanted);
}
// resampling_time_requested enforces a certain grouping multiple
NETDATA_DOUBLE resampling_divisor = 1.0;
size_t resampling_group = 1;
if (unlikely(resampling_time_requested > query_granularity)) {
// the points we should group to satisfy gtime
resampling_group = resampling_time_requested / query_granularity;
if (unlikely(resampling_time_requested % query_granularity))
resampling_group++;
query_debug_log(":resampling group %zu", resampling_group);
// adapt group according to resampling_group
if (unlikely(group < resampling_group)) {
group = resampling_group; // do not allow grouping below the desired one
query_debug_log(":group less res %zu", group);
}
if (unlikely(group % resampling_group)) {
group += resampling_group - (group % resampling_group); // make sure group is multiple of resampling_group
query_debug_log(":group mod res %zu", group);
}
// resampling_divisor = group / resampling_group;
resampling_divisor = (NETDATA_DOUBLE) (group * query_granularity) / (NETDATA_DOUBLE) resampling_time_requested;
query_debug_log(":resampling divisor " NETDATA_DOUBLE_FORMAT, resampling_divisor);
}
// now that we have group, align the requested timeframe to fit it.
if (aligned && before_wanted % (group * query_granularity)) {
if (before_is_aligned_to_db_end)
before_wanted -= before_wanted % (time_t)(group * query_granularity);
else
before_wanted += (time_t)(group * query_granularity) - before_wanted % (time_t)(group * query_granularity);
query_debug_log(":align before_wanted %ld", before_wanted);
}
after_wanted = before_wanted - (time_t)(points_wanted * group * query_granularity) + query_granularity;
query_debug_log(":final after_wanted %ld", after_wanted);
duration = before_wanted - after_wanted;
query_debug_log(":final duration %ld", duration + 1);
query_debug_log_fin();
internal_error(points_wanted != duration / (query_granularity * group) + 1,
"QUERY: points_wanted %zu is not points %zu",
points_wanted, (size_t)(duration / (query_granularity * group) + 1));
internal_error(group < resampling_group,
"QUERY: group %zu is less than the desired group points %zu",
group, resampling_group);
internal_error(group > resampling_group && group % resampling_group,
"QUERY: group %zu is not a multiple of the desired group points %zu",
group, resampling_group);
// -------------------------------------------------------------------------
// update QUERY_TARGET with our calculations
qt->window.after = after_wanted;
qt->window.before = before_wanted;
qt->window.relative = relative_period_requested;
qt->window.points = points_wanted;
qt->window.group = group;
qt->window.time_group_method = group_method;
qt->window.time_group_options = qt->request.time_group_options;
qt->window.query_granularity = query_granularity;
qt->window.resampling_group = resampling_group;
qt->window.resampling_divisor = resampling_divisor;
qt->window.options = options;
qt->window.tier = tier;
qt->window.aligned = aligned;
return true;
}
// ----------------------------------------------------------------------------
// group by
struct group_by_label_key {
DICTIONARY *values;
};
static void group_by_label_key_insert_cb(const DICTIONARY_ITEM *item __maybe_unused, void *value, void *data) {
// add the key to our r->label_keys global keys dictionary
DICTIONARY *label_keys = data;
dictionary_set(label_keys, dictionary_acquired_item_name(item), NULL, 0);
// create a dictionary for the values of this key
struct group_by_label_key *k = value;
k->values = dictionary_create_advanced(DICT_OPTION_SINGLE_THREADED | DICT_OPTION_DONT_OVERWRITE_VALUE, NULL, 0);
}
static void group_by_label_key_delete_cb(const DICTIONARY_ITEM *item __maybe_unused, void *value, void *data __maybe_unused) {
struct group_by_label_key *k = value;
dictionary_destroy(k->values);
}
static int rrdlabels_traversal_cb_to_group_by_label_key(const char *name, const char *value, RRDLABEL_SRC ls __maybe_unused, void *data) {
DICTIONARY *dl = data;
struct group_by_label_key *k = dictionary_set(dl, name, NULL, sizeof(struct group_by_label_key));
dictionary_set(k->values, value, NULL, 0);
return 1;
}
void rrdr_json_group_by_labels(BUFFER *wb, const char *key, RRDR *r, RRDR_OPTIONS options) {
if(!r->label_keys || !r->dl)
return;
buffer_json_member_add_object(wb, key);
void *t;
dfe_start_read(r->label_keys, t) {
buffer_json_member_add_array(wb, t_dfe.name);
for(size_t d = 0; d < r->d ;d++) {
if(!rrdr_dimension_should_be_exposed(r->od[d], options))
continue;
struct group_by_label_key *k = dictionary_get(r->dl[d], t_dfe.name);
if(k) {
buffer_json_add_array_item_array(wb);
void *tt;
dfe_start_read(k->values, tt) {
buffer_json_add_array_item_string(wb, tt_dfe.name);
}
dfe_done(tt);
buffer_json_array_close(wb);
}
else
buffer_json_add_array_item_string(wb, NULL);
}
buffer_json_array_close(wb);
}
dfe_done(t);
buffer_json_object_close(wb); // key
}
static void rrd2rrdr_set_timestamps(RRDR *r) {
QUERY_TARGET *qt = r->internal.qt;
internal_fatal(qt->window.points != r->n, "QUERY: mismatch to the number of points in qt and r");
r->view.group = qt->window.group;
r->view.update_every = (int) query_view_update_every(qt);
r->view.before = qt->window.before;
r->view.after = qt->window.after;
r->time_grouping.points_wanted = qt->window.points;
r->time_grouping.resampling_group = qt->window.resampling_group;
r->time_grouping.resampling_divisor = qt->window.resampling_divisor;
r->rows = qt->window.points;
size_t points_wanted = qt->window.points;
time_t after_wanted = qt->window.after;
time_t before_wanted = qt->window.before; (void)before_wanted;
time_t view_update_every = r->view.update_every;
time_t query_granularity = (time_t)(r->view.update_every / r->view.group);
size_t rrdr_line = 0;
time_t first_point_end_time = after_wanted + view_update_every - query_granularity;
time_t now_end_time = first_point_end_time;
while (rrdr_line < points_wanted) {
r->t[rrdr_line++] = now_end_time;
now_end_time += view_update_every;
}
internal_fatal(r->t[0] != first_point_end_time, "QUERY: wrong first timestamp in the query");
internal_error(r->t[points_wanted - 1] != before_wanted,
"QUERY: wrong last timestamp in the query, expected %ld, found %ld",
before_wanted, r->t[points_wanted - 1]);
}
static void query_group_by_make_dimension_key(BUFFER *key, RRDR_GROUP_BY group_by, size_t group_by_id, QUERY_TARGET *qt, QUERY_NODE *qn, QUERY_CONTEXT *qc, QUERY_INSTANCE *qi, QUERY_DIMENSION *qd __maybe_unused, QUERY_METRIC *qm, bool query_has_percentage_of_group) {
buffer_flush(key);
if(unlikely(!query_has_percentage_of_group && qm->status & RRDR_DIMENSION_HIDDEN)) {
buffer_strcat(key, "__hidden_dimensions__");
}
else if(unlikely(group_by & RRDR_GROUP_BY_SELECTED)) {
buffer_strcat(key, "selected");
}
else {
if (group_by & RRDR_GROUP_BY_DIMENSION) {
buffer_fast_strcat(key, "|", 1);
buffer_strcat(key, query_metric_name(qt, qm));
}
if (group_by & (RRDR_GROUP_BY_INSTANCE|RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE)) {
buffer_fast_strcat(key, "|", 1);
buffer_strcat(key, string2str(query_instance_id_fqdn(qi, qt->request.version)));
}
if (group_by & RRDR_GROUP_BY_LABEL) {
RRDLABELS *labels = rrdinstance_acquired_labels(qi->ria);
for (size_t l = 0; l < qt->group_by[group_by_id].used; l++) {
buffer_fast_strcat(key, "|", 1);
rrdlabels_get_value_to_buffer_or_unset(labels, key, qt->group_by[group_by_id].label_keys[l], "[unset]");
}
}
if (group_by & RRDR_GROUP_BY_NODE) {
buffer_fast_strcat(key, "|", 1);
buffer_strcat(key, qn->rrdhost->machine_guid);
}
if (group_by & RRDR_GROUP_BY_CONTEXT) {
buffer_fast_strcat(key, "|", 1);
buffer_strcat(key, rrdcontext_acquired_id(qc->rca));
}
if (group_by & RRDR_GROUP_BY_UNITS) {
buffer_fast_strcat(key, "|", 1);
buffer_strcat(key, query_target_has_percentage_units(qt) ? "%" : rrdinstance_acquired_units(qi->ria));
}
}
}
static void query_group_by_make_dimension_id(BUFFER *key, RRDR_GROUP_BY group_by, size_t group_by_id, QUERY_TARGET *qt, QUERY_NODE *qn, QUERY_CONTEXT *qc, QUERY_INSTANCE *qi, QUERY_DIMENSION *qd __maybe_unused, QUERY_METRIC *qm, bool query_has_percentage_of_group) {
buffer_flush(key);
if(unlikely(!query_has_percentage_of_group && qm->status & RRDR_DIMENSION_HIDDEN)) {
buffer_strcat(key, "__hidden_dimensions__");
}
else if(unlikely(group_by & RRDR_GROUP_BY_SELECTED)) {
buffer_strcat(key, "selected");
}
else {
if (group_by & RRDR_GROUP_BY_DIMENSION) {
buffer_strcat(key, query_metric_name(qt, qm));
}
if (group_by & (RRDR_GROUP_BY_INSTANCE|RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE)) {
if (buffer_strlen(key) != 0)
buffer_fast_strcat(key, ",", 1);
if (group_by & RRDR_GROUP_BY_NODE)
buffer_strcat(key, rrdinstance_acquired_id(qi->ria));
else
buffer_strcat(key, string2str(query_instance_id_fqdn(qi, qt->request.version)));
}
if (group_by & RRDR_GROUP_BY_LABEL) {
RRDLABELS *labels = rrdinstance_acquired_labels(qi->ria);
for (size_t l = 0; l < qt->group_by[group_by_id].used; l++) {
if (buffer_strlen(key) != 0)
buffer_fast_strcat(key, ",", 1);
rrdlabels_get_value_to_buffer_or_unset(labels, key, qt->group_by[group_by_id].label_keys[l], "[unset]");
}
}
if (group_by & RRDR_GROUP_BY_NODE) {
if (buffer_strlen(key) != 0)
buffer_fast_strcat(key, ",", 1);
buffer_strcat(key, qn->rrdhost->machine_guid);
}
if (group_by & RRDR_GROUP_BY_CONTEXT) {
if (buffer_strlen(key) != 0)
buffer_fast_strcat(key, ",", 1);
buffer_strcat(key, rrdcontext_acquired_id(qc->rca));
}
if (group_by & RRDR_GROUP_BY_UNITS) {
if (buffer_strlen(key) != 0)
buffer_fast_strcat(key, ",", 1);
buffer_strcat(key, query_target_has_percentage_units(qt) ? "%" : rrdinstance_acquired_units(qi->ria));
}
}
}
static void query_group_by_make_dimension_name(BUFFER *key, RRDR_GROUP_BY group_by, size_t group_by_id, QUERY_TARGET *qt, QUERY_NODE *qn, QUERY_CONTEXT *qc, QUERY_INSTANCE *qi, QUERY_DIMENSION *qd __maybe_unused, QUERY_METRIC *qm, bool query_has_percentage_of_group) {
buffer_flush(key);
if(unlikely(!query_has_percentage_of_group && qm->status & RRDR_DIMENSION_HIDDEN)) {
buffer_strcat(key, "__hidden_dimensions__");
}
else if(unlikely(group_by & RRDR_GROUP_BY_SELECTED)) {
buffer_strcat(key, "selected");
}
else {
if (group_by & RRDR_GROUP_BY_DIMENSION) {
buffer_strcat(key, query_metric_name(qt, qm));
}
if (group_by & (RRDR_GROUP_BY_INSTANCE|RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE)) {
if (buffer_strlen(key) != 0)
buffer_fast_strcat(key, ",", 1);
if (group_by & RRDR_GROUP_BY_NODE)
buffer_strcat(key, rrdinstance_acquired_name(qi->ria));
else
buffer_strcat(key, string2str(query_instance_name_fqdn(qi, qt->request.version)));
}
if (group_by & RRDR_GROUP_BY_LABEL) {
RRDLABELS *labels = rrdinstance_acquired_labels(qi->ria);
for (size_t l = 0; l < qt->group_by[group_by_id].used; l++) {
if (buffer_strlen(key) != 0)
buffer_fast_strcat(key, ",", 1);
rrdlabels_get_value_to_buffer_or_unset(labels, key, qt->group_by[group_by_id].label_keys[l], "[unset]");
}
}
if (group_by & RRDR_GROUP_BY_NODE) {
if (buffer_strlen(key) != 0)
buffer_fast_strcat(key, ",", 1);
buffer_strcat(key, rrdhost_hostname(qn->rrdhost));
}
if (group_by & RRDR_GROUP_BY_CONTEXT) {
if (buffer_strlen(key) != 0)
buffer_fast_strcat(key, ",", 1);
buffer_strcat(key, rrdcontext_acquired_id(qc->rca));
}
if (group_by & RRDR_GROUP_BY_UNITS) {
if (buffer_strlen(key) != 0)
buffer_fast_strcat(key, ",", 1);
buffer_strcat(key, query_target_has_percentage_units(qt) ? "%" : rrdinstance_acquired_units(qi->ria));
}
}
}
struct rrdr_group_by_entry {
size_t priority;
size_t count;
STRING *id;
STRING *name;
STRING *units;
RRDR_DIMENSION_FLAGS od;
DICTIONARY *dl;
};
static RRDR *rrd2rrdr_group_by_initialize(ONEWAYALLOC *owa, QUERY_TARGET *qt) {
RRDR *r_tmp = NULL;
RRDR_OPTIONS options = qt->window.options;
if(qt->request.version < 2) {
// v1 query
RRDR *r = rrdr_create(owa, qt, qt->query.used, qt->window.points);
if(unlikely(!r)) {
internal_error(true, "QUERY: cannot create RRDR for %s, after=%ld, before=%ld, dimensions=%u, points=%zu",
qt->id, qt->window.after, qt->window.before, qt->query.used, qt->window.points);
return NULL;
}
r->group_by.r = NULL;
for(size_t d = 0; d < qt->query.used ; d++) {
QUERY_METRIC *qm = query_metric(qt, d);
QUERY_DIMENSION *qd = query_dimension(qt, qm->link.query_dimension_id);
r->di[d] = rrdmetric_acquired_id_dup(qd->rma);
r->dn[d] = rrdmetric_acquired_name_dup(qd->rma);
}
rrd2rrdr_set_timestamps(r);
return r;
}
// v2 query
// parse all the group-by label keys
for(size_t g = 0; g < MAX_QUERY_GROUP_BY_PASSES ;g++) {
if (qt->request.group_by[g].group_by & RRDR_GROUP_BY_LABEL &&
qt->request.group_by[g].group_by_label && *qt->request.group_by[g].group_by_label)
qt->group_by[g].used = quoted_strings_splitter_query_group_by_label(
qt->request.group_by[g].group_by_label, qt->group_by[g].label_keys,
GROUP_BY_MAX_LABEL_KEYS);
if (!qt->group_by[g].used)
qt->request.group_by[g].group_by &= ~RRDR_GROUP_BY_LABEL;
}
// make sure there are valid group-by methods
for(size_t g = 0; g < MAX_QUERY_GROUP_BY_PASSES ;g++) {
if(!(qt->request.group_by[g].group_by & SUPPORTED_GROUP_BY_METHODS))
qt->request.group_by[g].group_by = (g == 0) ? RRDR_GROUP_BY_DIMENSION : RRDR_GROUP_BY_NONE;
}
bool query_has_percentage_of_group = query_target_has_percentage_of_group(qt);
// merge all group-by options to upper levels,
// so that the top level has all the groupings of the inner levels,
// and each subsequent level has all the groupings of its inner levels.
for(size_t g = 0; g < MAX_QUERY_GROUP_BY_PASSES - 1 ;g++) {
if(qt->request.group_by[g].group_by == RRDR_GROUP_BY_NONE)
continue;
if(qt->request.group_by[g].group_by == RRDR_GROUP_BY_SELECTED) {
for (size_t r = g + 1; r < MAX_QUERY_GROUP_BY_PASSES; r++)
qt->request.group_by[r].group_by = RRDR_GROUP_BY_NONE;
}
else {
for (size_t r = g + 1; r < MAX_QUERY_GROUP_BY_PASSES; r++) {
if (qt->request.group_by[r].group_by == RRDR_GROUP_BY_NONE)
continue;
if (qt->request.group_by[r].group_by != RRDR_GROUP_BY_SELECTED) {
if(qt->request.group_by[r].group_by & RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE)
qt->request.group_by[g].group_by |= RRDR_GROUP_BY_INSTANCE;
else
qt->request.group_by[g].group_by |= qt->request.group_by[r].group_by;
if(qt->request.group_by[r].group_by & RRDR_GROUP_BY_LABEL) {
for (size_t lr = 0; lr < qt->group_by[r].used; lr++) {
bool found = false;
for (size_t lg = 0; lg < qt->group_by[g].used; lg++) {
if (strcmp(qt->group_by[g].label_keys[lg], qt->group_by[r].label_keys[lr]) == 0) {
found = true;
break;
}
}
if (!found && qt->group_by[g].used < GROUP_BY_MAX_LABEL_KEYS * MAX_QUERY_GROUP_BY_PASSES)
qt->group_by[g].label_keys[qt->group_by[g].used++] = qt->group_by[r].label_keys[lr];
}
}
}
}
}
}
int added = 0;
RRDR *first_r = NULL, *last_r = NULL;
BUFFER *key = buffer_create(0, NULL);
struct rrdr_group_by_entry *entries = onewayalloc_mallocz(owa, qt->query.used * sizeof(struct rrdr_group_by_entry));
DICTIONARY *groups = dictionary_create(DICT_OPTION_SINGLE_THREADED | DICT_OPTION_DONT_OVERWRITE_VALUE);
DICTIONARY *label_keys = NULL;
for(size_t g = 0; g < MAX_QUERY_GROUP_BY_PASSES ;g++) {
RRDR_GROUP_BY group_by = qt->request.group_by[g].group_by;
RRDR_GROUP_BY_FUNCTION aggregation_method = qt->request.group_by[g].aggregation;
if(group_by == RRDR_GROUP_BY_NONE)
break;
memset(entries, 0, qt->query.used * sizeof(struct rrdr_group_by_entry));
dictionary_flush(groups);
added = 0;
size_t hidden_dimensions = 0;
bool final_grouping = (g == MAX_QUERY_GROUP_BY_PASSES - 1 || qt->request.group_by[g + 1].group_by == RRDR_GROUP_BY_NONE) ? true : false;
if (final_grouping && (options & RRDR_OPTION_GROUP_BY_LABELS))
label_keys = dictionary_create_advanced(DICT_OPTION_SINGLE_THREADED | DICT_OPTION_DONT_OVERWRITE_VALUE, NULL, 0);
QUERY_INSTANCE *last_qi = NULL;
size_t priority = 0;
time_t update_every_max = 0;
for (size_t d = 0; d < qt->query.used; d++) {
QUERY_METRIC *qm = query_metric(qt, d);
QUERY_DIMENSION *qd = query_dimension(qt, qm->link.query_dimension_id);
QUERY_INSTANCE *qi = query_instance(qt, qm->link.query_instance_id);
QUERY_CONTEXT *qc = query_context(qt, qm->link.query_context_id);
QUERY_NODE *qn = query_node(qt, qm->link.query_node_id);
if (qi != last_qi) {
last_qi = qi;
time_t update_every = rrdinstance_acquired_update_every(qi->ria);
if (update_every > update_every_max)
update_every_max = update_every;
}
priority = qd->priority;
if(qm->status & RRDR_DIMENSION_HIDDEN)
hidden_dimensions++;
// --------------------------------------------------------------------
// generate the group by key
query_group_by_make_dimension_key(key, group_by, g, qt, qn, qc, qi, qd, qm, query_has_percentage_of_group);
// lookup the key in the dictionary
int pos = -1;
int *set = dictionary_set(groups, buffer_tostring(key), &pos, sizeof(pos));
if (*set == -1) {
// the key just added to the dictionary
*set = pos = added++;
// ----------------------------------------------------------------
// generate the dimension id
query_group_by_make_dimension_id(key, group_by, g, qt, qn, qc, qi, qd, qm, query_has_percentage_of_group);
entries[pos].id = string_strdupz(buffer_tostring(key));
// ----------------------------------------------------------------
// generate the dimension name
query_group_by_make_dimension_name(key, group_by, g, qt, qn, qc, qi, qd, qm, query_has_percentage_of_group);
entries[pos].name = string_strdupz(buffer_tostring(key));
// add the rest of the info
entries[pos].units = rrdinstance_acquired_units_dup(qi->ria);
entries[pos].priority = priority;
if (label_keys) {
entries[pos].dl = dictionary_create_advanced(
DICT_OPTION_SINGLE_THREADED | DICT_OPTION_FIXED_SIZE | DICT_OPTION_DONT_OVERWRITE_VALUE,
NULL, sizeof(struct group_by_label_key));
dictionary_register_insert_callback(entries[pos].dl, group_by_label_key_insert_cb, label_keys);
dictionary_register_delete_callback(entries[pos].dl, group_by_label_key_delete_cb, label_keys);
}
} else {
// the key found in the dictionary
pos = *set;
}
entries[pos].count++;
if (unlikely(priority < entries[pos].priority))
entries[pos].priority = priority;
if(g > 0)
last_r->dgbs[qm->grouped_as.slot] = pos;
else
qm->grouped_as.first_slot = pos;
qm->grouped_as.slot = pos;
qm->grouped_as.id = entries[pos].id;
qm->grouped_as.name = entries[pos].name;
qm->grouped_as.units = entries[pos].units;
// copy the dimension flags decided by the query target
// we need this, because if a dimension is explicitly selected
// the query target adds to it the non-zero flag
qm->status |= RRDR_DIMENSION_GROUPED;
if(query_has_percentage_of_group)
// when the query has percentage of group
// there will be no hidden dimensions in the final query,
// so we have to remove the hidden flag from all dimensions
entries[pos].od |= qm->status & ~RRDR_DIMENSION_HIDDEN;
else
entries[pos].od |= qm->status;
if (entries[pos].dl)
rrdlabels_walkthrough_read(rrdinstance_acquired_labels(qi->ria),
rrdlabels_traversal_cb_to_group_by_label_key, entries[pos].dl);
}
RRDR *r = rrdr_create(owa, qt, added, qt->window.points);
if (!r) {
internal_error(true,
"QUERY: cannot create group by RRDR for %s, after=%ld, before=%ld, dimensions=%d, points=%zu",
qt->id, qt->window.after, qt->window.before, added, qt->window.points);
goto cleanup;
}
// prevent double free at cleanup in case of error
added = 0;
// link this RRDR
if(!last_r)
first_r = last_r = r;
else
last_r->group_by.r = r;
last_r = r;
rrd2rrdr_set_timestamps(r);
r->dp = onewayalloc_callocz(owa, r->d, sizeof(*r->dp));
r->dview = onewayalloc_callocz(owa, r->d, sizeof(*r->dview));
r->dgbc = onewayalloc_callocz(owa, r->d, sizeof(*r->dgbc));
r->gbc = onewayalloc_callocz(owa, r->n * r->d, sizeof(*r->gbc));
r->dqp = onewayalloc_callocz(owa, r->d, sizeof(STORAGE_POINT));
if(hidden_dimensions && ((group_by & RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE) || (aggregation_method == RRDR_GROUP_BY_FUNCTION_PERCENTAGE)))
// this is where we are going to group the hidden dimensions
r->vh = onewayalloc_mallocz(owa, r->n * r->d * sizeof(*r->vh));
if(!final_grouping)
// this is where we are going to store the slot in the next RRDR
// that we are going to group by the dimension of this RRDR
r->dgbs = onewayalloc_callocz(owa, r->d, sizeof(*r->dgbs));
if (label_keys) {
r->dl = onewayalloc_callocz(owa, r->d, sizeof(DICTIONARY *));
r->label_keys = label_keys;
label_keys = NULL;
}
// zero r (dimension options, names, and ids)
// this is required, because group-by may lead to empty dimensions
for (size_t d = 0; d < r->d; d++) {
r->di[d] = entries[d].id;
r->dn[d] = entries[d].name;
r->od[d] = entries[d].od;
r->du[d] = entries[d].units;
r->dp[d] = entries[d].priority;
r->dgbc[d] = entries[d].count;
if (r->dl)
r->dl[d] = entries[d].dl;
}
// initialize partial trimming
r->partial_data_trimming.max_update_every = update_every_max * 2;
r->partial_data_trimming.expected_after =
(!query_target_aggregatable(qt) &&
qt->window.before >= qt->window.now - r->partial_data_trimming.max_update_every) ?
qt->window.before - r->partial_data_trimming.max_update_every :
qt->window.before;
r->partial_data_trimming.trimmed_after = qt->window.before;
// make all values empty
for (size_t i = 0; i != r->n; i++) {
NETDATA_DOUBLE *cn = &r->v[i * r->d];
RRDR_VALUE_FLAGS *co = &r->o[i * r->d];
NETDATA_DOUBLE *ar = &r->ar[i * r->d];
NETDATA_DOUBLE *vh = r->vh ? &r->vh[i * r->d] : NULL;
for (size_t d = 0; d < r->d; d++) {
cn[d] = NAN;
ar[d] = 0.0;
co[d] = RRDR_VALUE_EMPTY;
if(vh)
vh[d] = NAN;
}
}
}
if(!first_r || !last_r)
goto cleanup;
r_tmp = rrdr_create(owa, qt, 1, qt->window.points);
if (!r_tmp) {
internal_error(true,
"QUERY: cannot create group by temporary RRDR for %s, after=%ld, before=%ld, dimensions=%d, points=%zu",
qt->id, qt->window.after, qt->window.before, 1, qt->window.points);
goto cleanup;
}
rrd2rrdr_set_timestamps(r_tmp);
r_tmp->group_by.r = first_r;
cleanup:
if(!first_r || !last_r || !r_tmp) {
if(r_tmp) {
r_tmp->group_by.r = NULL;
rrdr_free(owa, r_tmp);
}
if(first_r) {
RRDR *r = first_r;
while (r) {
r_tmp = r->group_by.r;
r->group_by.r = NULL;
rrdr_free(owa, r);
r = r_tmp;
}
}
if(entries && added) {
for (int d = 0; d < added; d++) {
string_freez(entries[d].id);
string_freez(entries[d].name);
string_freez(entries[d].units);
dictionary_destroy(entries[d].dl);
}
}
dictionary_destroy(label_keys);
first_r = last_r = r_tmp = NULL;
}
buffer_free(key);
onewayalloc_freez(owa, entries);
dictionary_destroy(groups);
return r_tmp;
}
static void rrd2rrdr_group_by_add_metric(RRDR *r_dst, size_t d_dst, RRDR *r_tmp, size_t d_tmp,
RRDR_GROUP_BY_FUNCTION group_by_aggregate_function,
STORAGE_POINT *query_points, size_t pass __maybe_unused) {
if(!r_tmp || r_dst == r_tmp || !(r_tmp->od[d_tmp] & RRDR_DIMENSION_QUERIED))
return;
internal_fatal(r_dst->n != r_tmp->n, "QUERY: group-by source and destination do not have the same number of rows");
internal_fatal(d_dst >= r_dst->d, "QUERY: group-by destination dimension number exceeds destination RRDR size");
internal_fatal(d_tmp >= r_tmp->d, "QUERY: group-by source dimension number exceeds source RRDR size");
internal_fatal(!r_dst->dqp, "QUERY: group-by destination is not properly prepared (missing dqp array)");
internal_fatal(!r_dst->gbc, "QUERY: group-by destination is not properly prepared (missing gbc array)");
bool hidden_dimension_on_percentage_of_group = (r_tmp->od[d_tmp] & RRDR_DIMENSION_HIDDEN) && r_dst->vh;
if(!hidden_dimension_on_percentage_of_group) {
r_dst->od[d_dst] |= r_tmp->od[d_tmp];
storage_point_merge_to(r_dst->dqp[d_dst], *query_points);
}
// do the group_by
for(size_t i = 0; i != rrdr_rows(r_tmp) ; i++) {
size_t idx_tmp = i * r_tmp->d + d_tmp;
NETDATA_DOUBLE n_tmp = r_tmp->v[ idx_tmp ];
RRDR_VALUE_FLAGS o_tmp = r_tmp->o[ idx_tmp ];
NETDATA_DOUBLE ar_tmp = r_tmp->ar[ idx_tmp ];
if(o_tmp & RRDR_VALUE_EMPTY)
continue;
size_t idx_dst = i * r_dst->d + d_dst;
NETDATA_DOUBLE *cn = (hidden_dimension_on_percentage_of_group) ? &r_dst->vh[ idx_dst ] : &r_dst->v[ idx_dst ];
RRDR_VALUE_FLAGS *co = &r_dst->o[ idx_dst ];
NETDATA_DOUBLE *ar = &r_dst->ar[ idx_dst ];
uint32_t *gbc = &r_dst->gbc[ idx_dst ];
switch(group_by_aggregate_function) {
default:
case RRDR_GROUP_BY_FUNCTION_AVERAGE:
case RRDR_GROUP_BY_FUNCTION_SUM:
case RRDR_GROUP_BY_FUNCTION_PERCENTAGE:
if(isnan(*cn))
*cn = n_tmp;
else
*cn += n_tmp;
break;
case RRDR_GROUP_BY_FUNCTION_MIN:
if(isnan(*cn) || n_tmp < *cn)
*cn = n_tmp;
break;
case RRDR_GROUP_BY_FUNCTION_MAX:
if(isnan(*cn) || n_tmp > *cn)
*cn = n_tmp;
break;
}
if(!hidden_dimension_on_percentage_of_group) {
*co &= ~RRDR_VALUE_EMPTY;
*co |= (o_tmp & (RRDR_VALUE_RESET | RRDR_VALUE_PARTIAL));
*ar += ar_tmp;
(*gbc)++;
}
}
}
static void rrdr2rrdr_group_by_partial_trimming(RRDR *r) {
time_t trimmable_after = r->partial_data_trimming.expected_after;
// find the point just before the trimmable ones
ssize_t i = (ssize_t)r->n - 1;
for( ; i >= 0 ;i--) {
if (r->t[i] < trimmable_after)
break;
}
if(unlikely(i < 0))
return;
// internal_error(true, "Found trimmable index %zd (from 0 to %zu)", i, r->n - 1);
size_t last_row_gbc = 0;
for (; i < (ssize_t)r->n; i++) {
size_t row_gbc = 0;
for (size_t d = 0; d < r->d; d++) {
if (unlikely(!(r->od[d] & RRDR_DIMENSION_QUERIED)))
continue;
row_gbc += r->gbc[ i * r->d + d ];
}
// internal_error(true, "GBC of index %zd is %zu", i, row_gbc);
if (unlikely(r->t[i] >= trimmable_after && (row_gbc < last_row_gbc || !row_gbc))) {
// discard the rest of the points
// internal_error(true, "Discarding points %zd to %zu", i, r->n - 1);
r->partial_data_trimming.trimmed_after = r->t[i];
r->rows = i;
break;
}
else
last_row_gbc = row_gbc;
}
}
static void rrdr2rrdr_group_by_calculate_percentage_of_group(RRDR *r) {
if(!r->vh)
return;
if(query_target_aggregatable(r->internal.qt) && query_has_group_by_aggregation_percentage(r->internal.qt))
return;
for(size_t i = 0; i < r->n ;i++) {
NETDATA_DOUBLE *cn = &r->v[ i * r->d ];
NETDATA_DOUBLE *ch = &r->vh[ i * r->d ];
for(size_t d = 0; d < r->d ;d++) {
NETDATA_DOUBLE n = cn[d];
NETDATA_DOUBLE h = ch[d];
if(isnan(n))
cn[d] = 0.0;
else if(isnan(h))
cn[d] = 100.0;
else
cn[d] = n * 100.0 / (n + h);
}
}
}
static void rrd2rrdr_convert_values_to_percentage_of_total(RRDR *r) {
if(!(r->internal.qt->window.options & RRDR_OPTION_PERCENTAGE) || query_target_aggregatable(r->internal.qt))
return;
size_t global_min_max_values = 0;
NETDATA_DOUBLE global_min = NAN, global_max = NAN;
for(size_t i = 0; i != r->n ;i++) {
NETDATA_DOUBLE *cn = &r->v[ i * r->d ];
RRDR_VALUE_FLAGS *co = &r->o[ i * r->d ];
NETDATA_DOUBLE total = 0;
for (size_t d = 0; d < r->d; d++) {
if (unlikely(!(r->od[d] & RRDR_DIMENSION_QUERIED)))
continue;
if(co[d] & RRDR_VALUE_EMPTY)
continue;
total += cn[d];
}
if(total == 0.0)
total = 1.0;
for (size_t d = 0; d < r->d; d++) {
if (unlikely(!(r->od[d] & RRDR_DIMENSION_QUERIED)))
continue;
if(co[d] & RRDR_VALUE_EMPTY)
continue;
NETDATA_DOUBLE n = cn[d];
n = cn[d] = n * 100.0 / total;
if(unlikely(!global_min_max_values++))
global_min = global_max = n;
else {
if(n < global_min)
global_min = n;
if(n > global_max)
global_max = n;
}
}
}
r->view.min = global_min;
r->view.max = global_max;
if(!r->dview)
// v1 query
return;
// v2 query
for (size_t d = 0; d < r->d; d++) {
if (unlikely(!(r->od[d] & RRDR_DIMENSION_QUERIED)))
continue;
size_t count = 0;
NETDATA_DOUBLE min = 0.0, max = 0.0, sum = 0.0, ars = 0.0;
for(size_t i = 0; i != r->rows ;i++) { // we use r->rows to respect trimming
size_t idx = i * r->d + d;
RRDR_VALUE_FLAGS o = r->o[ idx ];
if (o & RRDR_VALUE_EMPTY)
continue;
NETDATA_DOUBLE ar = r->ar[ idx ];
ars += ar;
NETDATA_DOUBLE n = r->v[ idx ];
sum += n;
if(!count++)
min = max = n;
else {
if(n < min)
min = n;
if(n > max)
max = n;
}
}
r->dview[d] = (STORAGE_POINT) {
.sum = sum,
.count = count,
.min = min,
.max = max,
.anomaly_count = (size_t)(ars * (NETDATA_DOUBLE)count),
};
}
}
static RRDR *rrd2rrdr_group_by_finalize(RRDR *r_tmp) {
QUERY_TARGET *qt = r_tmp->internal.qt;
if(!r_tmp->group_by.r) {
// v1 query
rrd2rrdr_convert_values_to_percentage_of_total(r_tmp);
return r_tmp;
}
// v2 query
// do the additional passes on RRDRs
RRDR *last_r = r_tmp->group_by.r;
rrdr2rrdr_group_by_calculate_percentage_of_group(last_r);
RRDR *r = last_r->group_by.r;
size_t pass = 0;
while(r) {
pass++;
for(size_t d = 0; d < last_r->d ;d++) {
rrd2rrdr_group_by_add_metric(r, last_r->dgbs[d], last_r, d,
qt->request.group_by[pass].aggregation,
&last_r->dqp[d], pass);
}
rrdr2rrdr_group_by_calculate_percentage_of_group(r);
last_r = r;
r = last_r->group_by.r;
}
// free all RRDRs except the last one
r = r_tmp;
while(r != last_r) {
r_tmp = r->group_by.r;
r->group_by.r = NULL;
rrdr_free(r->internal.owa, r);
r = r_tmp;
}
r = last_r;
// find the final aggregation
RRDR_GROUP_BY_FUNCTION aggregation = qt->request.group_by[0].aggregation;
for(size_t g = 0; g < MAX_QUERY_GROUP_BY_PASSES ;g++)
if(qt->request.group_by[g].group_by != RRDR_GROUP_BY_NONE)
aggregation = qt->request.group_by[g].aggregation;
if(!query_target_aggregatable(qt) && r->partial_data_trimming.expected_after < qt->window.before)
rrdr2rrdr_group_by_partial_trimming(r);
// apply averaging, remove RRDR_VALUE_EMPTY, find the non-zero dimensions, min and max
size_t global_min_max_values = 0;
size_t dimensions_nonzero = 0;
NETDATA_DOUBLE global_min = NAN, global_max = NAN;
for (size_t d = 0; d < r->d; d++) {
if (unlikely(!(r->od[d] & RRDR_DIMENSION_QUERIED)))
continue;
size_t points_nonzero = 0;
NETDATA_DOUBLE min = 0, max = 0, sum = 0, ars = 0;
size_t count = 0;
for(size_t i = 0; i != r->n ;i++) {
size_t idx = i * r->d + d;
NETDATA_DOUBLE *cn = &r->v[ idx ];
RRDR_VALUE_FLAGS *co = &r->o[ idx ];
NETDATA_DOUBLE *ar = &r->ar[ idx ];
uint32_t gbc = r->gbc[ idx ];
if(likely(gbc)) {
*co &= ~RRDR_VALUE_EMPTY;
if(gbc != r->dgbc[d])
*co |= RRDR_VALUE_PARTIAL;
NETDATA_DOUBLE n;
sum += *cn;
ars += *ar;
if(aggregation == RRDR_GROUP_BY_FUNCTION_AVERAGE && !query_target_aggregatable(qt))
n = (*cn /= gbc);
else
n = *cn;
if(!query_target_aggregatable(qt))
*ar /= gbc;
if(islessgreater(n, 0.0))
points_nonzero++;
if(unlikely(!count))
min = max = n;
else {
if(n < min)
min = n;
if(n > max)
max = n;
}
if(unlikely(!global_min_max_values++))
global_min = global_max = n;
else {
if(n < global_min)
global_min = n;
if(n > global_max)
global_max = n;
}
count += gbc;
}
}
if(points_nonzero) {
r->od[d] |= RRDR_DIMENSION_NONZERO;
dimensions_nonzero++;
}
r->dview[d] = (STORAGE_POINT) {
.sum = sum,
.count = count,
.min = min,
.max = max,
.anomaly_count = (size_t)(ars * RRDR_DVIEW_ANOMALY_COUNT_MULTIPLIER / 100.0),
};
}
r->view.min = global_min;
r->view.max = global_max;
if(!dimensions_nonzero && (qt->window.options & RRDR_OPTION_NONZERO)) {
// all dimensions are zero
// remove the nonzero option
qt->window.options &= ~RRDR_OPTION_NONZERO;
}
rrd2rrdr_convert_values_to_percentage_of_total(r);
// update query instance counts in query host and query context
{
size_t h = 0, c = 0, i = 0;
for(; h < qt->nodes.used ; h++) {
QUERY_NODE *qn = &qt->nodes.array[h];
for(; c < qt->contexts.used ;c++) {
QUERY_CONTEXT *qc = &qt->contexts.array[c];
if(!rrdcontext_acquired_belongs_to_host(qc->rca, qn->rrdhost))
break;
for(; i < qt->instances.used ;i++) {
QUERY_INSTANCE *qi = &qt->instances.array[i];
if(!rrdinstance_acquired_belongs_to_context(qi->ria, qc->rca))
break;
if(qi->metrics.queried) {
qc->instances.queried++;
qn->instances.queried++;
}
else if(qi->metrics.failed) {
qc->instances.failed++;
qn->instances.failed++;
}
}
}
}
}
return r;
}
// ----------------------------------------------------------------------------
// query entry point
RRDR *rrd2rrdr_legacy(
ONEWAYALLOC *owa,
RRDSET *st, size_t points, time_t after, time_t before,
RRDR_TIME_GROUPING group_method, time_t resampling_time, RRDR_OPTIONS options, const char *dimensions,
const char *group_options, time_t timeout_ms, size_t tier, QUERY_SOURCE query_source,
STORAGE_PRIORITY priority) {
QUERY_TARGET_REQUEST qtr = {
.version = 1,
.st = st,
.points = points,
.after = after,
.before = before,
.time_group_method = group_method,
.resampling_time = resampling_time,
.options = options,
.dimensions = dimensions,
.time_group_options = group_options,
.timeout_ms = timeout_ms,
.tier = tier,
.query_source = query_source,
.priority = priority,
};
QUERY_TARGET *qt = query_target_create(&qtr);
RRDR *r = rrd2rrdr(owa, qt);
if(!r) {
query_target_release(qt);
return NULL;
}
r->internal.release_with_rrdr_qt = qt;
return r;
}
RRDR *rrd2rrdr(ONEWAYALLOC *owa, QUERY_TARGET *qt) {
if(!qt || !owa)
return NULL;
// qt.window members are the WANTED ones.
// qt.request members are the REQUESTED ones.
RRDR *r_tmp = rrd2rrdr_group_by_initialize(owa, qt);
if(!r_tmp)
return NULL;
// the RRDR we group-by at
RRDR *r = (r_tmp->group_by.r) ? r_tmp->group_by.r : r_tmp;
// the final RRDR to return to callers
RRDR *last_r = r_tmp;
while(last_r->group_by.r)
last_r = last_r->group_by.r;
if(qt->window.relative)
last_r->view.flags |= RRDR_RESULT_FLAG_RELATIVE;
else
last_r->view.flags |= RRDR_RESULT_FLAG_ABSOLUTE;
// -------------------------------------------------------------------------
// assign the processor functions
rrdr_set_grouping_function(r_tmp, qt->window.time_group_method);
// allocate any memory required by the grouping method
r_tmp->time_grouping.create(r_tmp, qt->window.time_group_options);
// -------------------------------------------------------------------------
// do the work for each dimension
time_t max_after = 0, min_before = 0;
size_t max_rows = 0;
long dimensions_used = 0, dimensions_nonzero = 0;
size_t last_db_points_read = 0;
size_t last_result_points_generated = 0;
internal_fatal(released_ops, "QUERY: released_ops should be NULL when the query starts");
query_progress_set_finish_line(qt->request.transaction, qt->query.used);
QUERY_ENGINE_OPS **ops = NULL;
if(qt->query.used)
ops = onewayalloc_callocz(owa, qt->query.used, sizeof(QUERY_ENGINE_OPS *));
size_t capacity = libuv_worker_threads * 10;
size_t max_queries_to_prepare = (qt->query.used > (capacity - 1)) ? (capacity - 1) : qt->query.used;
size_t queries_prepared = 0;
while(queries_prepared < max_queries_to_prepare) {
// preload another query
ops[queries_prepared] = rrd2rrdr_query_ops_prep(r_tmp, queries_prepared);
queries_prepared++;
}
QUERY_NODE *last_qn = NULL;
usec_t last_ut = now_monotonic_usec();
usec_t last_qn_ut = last_ut;
for(size_t d = 0; d < qt->query.used ; d++) {
QUERY_METRIC *qm = query_metric(qt, d);
QUERY_DIMENSION *qd = query_dimension(qt, qm->link.query_dimension_id);
QUERY_INSTANCE *qi = query_instance(qt, qm->link.query_instance_id);
QUERY_CONTEXT *qc = query_context(qt, qm->link.query_context_id);
QUERY_NODE *qn = query_node(qt, qm->link.query_node_id);
usec_t now_ut = last_ut;
if(qn != last_qn) {
if(last_qn)
last_qn->duration_ut = now_ut - last_qn_ut;
last_qn = qn;
last_qn_ut = now_ut;
}
if(queries_prepared < qt->query.used) {
// preload another query
ops[queries_prepared] = rrd2rrdr_query_ops_prep(r_tmp, queries_prepared);
queries_prepared++;
}
size_t dim_in_rrdr_tmp = (r_tmp != r) ? 0 : d;
// set the query target dimension options to rrdr
r_tmp->od[dim_in_rrdr_tmp] = qm->status;
// reset the grouping for the new dimension
r_tmp->time_grouping.reset(r_tmp);
if(ops[d]) {
rrd2rrdr_query_execute(r_tmp, dim_in_rrdr_tmp, ops[d]);
r_tmp->od[dim_in_rrdr_tmp] |= RRDR_DIMENSION_QUERIED;
now_ut = now_monotonic_usec();
qm->duration_ut = now_ut - last_ut;
last_ut = now_ut;
if(r_tmp != r) {
// copy back whatever got updated from the temporary r
// the query updates RRDR_DIMENSION_NONZERO
qm->status = r_tmp->od[dim_in_rrdr_tmp];
// the query updates these
r->view.min = r_tmp->view.min;
r->view.max = r_tmp->view.max;
r->view.after = r_tmp->view.after;
r->view.before = r_tmp->view.before;
r->rows = r_tmp->rows;
rrd2rrdr_group_by_add_metric(r, qm->grouped_as.first_slot, r_tmp, dim_in_rrdr_tmp,
qt->request.group_by[0].aggregation, &qm->query_points, 0);
}
rrd2rrdr_query_ops_release(ops[d]); // reuse this ops allocation
ops[d] = NULL;
qi->metrics.queried++;
qc->metrics.queried++;
qn->metrics.queried++;
qd->status |= QUERY_STATUS_QUERIED;
qm->status |= RRDR_DIMENSION_QUERIED;
if(qt->request.version >= 2) {
// we need to make the query points positive now
// since we will aggregate it across multiple dimensions
storage_point_make_positive(qm->query_points);
storage_point_merge_to(qi->query_points, qm->query_points);
storage_point_merge_to(qc->query_points, qm->query_points);
storage_point_merge_to(qn->query_points, qm->query_points);
storage_point_merge_to(qt->query_points, qm->query_points);
}
}
else {
qi->metrics.failed++;
qc->metrics.failed++;
qn->metrics.failed++;
qd->status |= QUERY_STATUS_FAILED;
qm->status |= RRDR_DIMENSION_FAILED;
continue;
}
global_statistics_rrdr_query_completed(
1,
r_tmp->stats.db_points_read - last_db_points_read,
r_tmp->stats.result_points_generated - last_result_points_generated,
qt->request.query_source);
last_db_points_read = r_tmp->stats.db_points_read;
last_result_points_generated = r_tmp->stats.result_points_generated;
if(qm->status & RRDR_DIMENSION_NONZERO)
dimensions_nonzero++;
// verify all dimensions are aligned
if(unlikely(!dimensions_used)) {
min_before = r->view.before;
max_after = r->view.after;
max_rows = r->rows;
}
else {
if(r->view.after != max_after) {
internal_error(true, "QUERY: 'after' mismatch between dimensions for chart '%s': max is %zu, dimension '%s' has %zu",
rrdinstance_acquired_id(qi->ria), (size_t)max_after, rrdmetric_acquired_id(qd->rma), (size_t)r->view.after);
r->view.after = (r->view.after > max_after) ? r->view.after : max_after;
}
if(r->view.before != min_before) {
internal_error(true, "QUERY: 'before' mismatch between dimensions for chart '%s': max is %zu, dimension '%s' has %zu",
rrdinstance_acquired_id(qi->ria), (size_t)min_before, rrdmetric_acquired_id(qd->rma), (size_t)r->view.before);
r->view.before = (r->view.before < min_before) ? r->view.before : min_before;
}
if(r->rows != max_rows) {
internal_error(true, "QUERY: 'rows' mismatch between dimensions for chart '%s': max is %zu, dimension '%s' has %zu",
rrdinstance_acquired_id(qi->ria), (size_t)max_rows, rrdmetric_acquired_id(qd->rma), (size_t)r->rows);
r->rows = (r->rows > max_rows) ? r->rows : max_rows;
}
}
dimensions_used++;
bool cancel = false;
if (qt->request.interrupt_callback && qt->request.interrupt_callback(qt->request.interrupt_callback_data)) {
cancel = true;
nd_log(NDLS_ACCESS, NDLP_NOTICE, "QUERY INTERRUPTED");
}
if (qt->request.timeout_ms && ((NETDATA_DOUBLE)(now_ut - qt->timings.received_ut) / 1000.0) > (NETDATA_DOUBLE)qt->request.timeout_ms) {
cancel = true;
nd_log(NDLS_ACCESS, NDLP_WARNING, "QUERY CANCELED RUNTIME EXCEEDED %0.2f ms (LIMIT %lld ms)",
(NETDATA_DOUBLE)(now_ut - qt->timings.received_ut) / 1000.0, (long long)qt->request.timeout_ms);
}
if(cancel) {
r->view.flags |= RRDR_RESULT_FLAG_CANCEL;
for(size_t i = d + 1; i < queries_prepared ; i++) {
if(ops[i]) {
query_planer_finalize_remaining_plans(ops[i]);
rrd2rrdr_query_ops_release(ops[i]);
ops[i] = NULL;
}
}
break;
}
else
query_progress_done_step(qt->request.transaction, 1);
}
// free all resources used by the grouping method
r_tmp->time_grouping.free(r_tmp);
// get the final RRDR to send to the caller
r = rrd2rrdr_group_by_finalize(r_tmp);
#ifdef NETDATA_INTERNAL_CHECKS
if (dimensions_used && !(r->view.flags & RRDR_RESULT_FLAG_CANCEL)) {
if(r->internal.log)
rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.time_group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group,
qt->window.after, qt->request.after, qt->window.before, qt->request.before,
qt->request.points, qt->window.points, /*after_slot, before_slot,*/
r->internal.log);
if(r->rows != qt->window.points)
rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.time_group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group,
qt->window.after, qt->request.after, qt->window.before, qt->request.before,
qt->request.points, qt->window.points, /*after_slot, before_slot,*/
"got 'points' is not wanted 'points'");
if(qt->window.aligned && (r->view.before % query_view_update_every(qt)) != 0)
rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.time_group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group,
qt->window.after, qt->request.after, qt->window.before, qt->request.before,
qt->request.points, qt->window.points, /*after_slot, before_slot,*/
"'before' is not aligned but alignment is required");
// 'after' should not be aligned, since we start inside the first group
//if(qt->window.aligned && (r->after % group) != 0)
// rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group, qt->window.after, after_requested, before_wanted, before_requested, points_requested, points_wanted, after_slot, before_slot, "'after' is not aligned but alignment is required");
if(r->view.before != qt->window.before)
rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.time_group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group,
qt->window.after, qt->request.after, qt->window.before, qt->request.before,
qt->request.points, qt->window.points, /*after_slot, before_slot,*/
"chart is not aligned to requested 'before'");
if(r->view.before != qt->window.before)
rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.time_group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group,
qt->window.after, qt->request.after, qt->window.before, qt->request.before,
qt->request.points, qt->window.points, /*after_slot, before_slot,*/
"got 'before' is not wanted 'before'");
// reported 'after' varies, depending on group
if(r->view.after != qt->window.after)
rrd2rrdr_log_request_response_metadata(r, qt->window.options, qt->window.time_group_method, qt->window.aligned, qt->window.group, qt->request.resampling_time, qt->window.resampling_group,
qt->window.after, qt->request.after, qt->window.before, qt->request.before,
qt->request.points, qt->window.points, /*after_slot, before_slot,*/
"got 'after' is not wanted 'after'");
}
#endif
// free the query pipelining ops
for(size_t d = 0; d < qt->query.used ; d++) {
rrd2rrdr_query_ops_release(ops[d]);
ops[d] = NULL;
}
rrd2rrdr_query_ops_freeall(r);
internal_fatal(released_ops, "QUERY: released_ops should be NULL when the query ends");
onewayalloc_freez(owa, ops);
if(likely(dimensions_used && (qt->window.options & RRDR_OPTION_NONZERO) && !dimensions_nonzero))
// when all the dimensions are zero, we should return all of them
qt->window.options &= ~RRDR_OPTION_NONZERO;
qt->timings.executed_ut = now_monotonic_usec();
return r;
}