// 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"
// ----------------------------------------------------------------------------
static struct {
const char *name;
uint32_t hash;
RRDR_GROUPING value;
// 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,
.init = NULL,
.create= grouping_create_average,
.reset = grouping_reset_average,
.free = grouping_free_average,
.add = grouping_add_average,
.flush = grouping_flush_average,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "mean", // alias on 'average'
.hash = 0,
.value = RRDR_GROUPING_AVERAGE,
.init = NULL,
.create= grouping_create_average,
.reset = grouping_reset_average,
.free = grouping_free_average,
.add = grouping_add_average,
.flush = grouping_flush_average,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean1",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN1,
.init = NULL,
.create= grouping_create_trimmed_mean1,
.reset = grouping_reset_trimmed_mean,
.free = grouping_free_trimmed_mean,
.add = grouping_add_trimmed_mean,
.flush = grouping_flush_trimmed_mean,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean2",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN2,
.init = NULL,
.create= grouping_create_trimmed_mean2,
.reset = grouping_reset_trimmed_mean,
.free = grouping_free_trimmed_mean,
.add = grouping_add_trimmed_mean,
.flush = grouping_flush_trimmed_mean,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean3",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN3,
.init = NULL,
.create= grouping_create_trimmed_mean3,
.reset = grouping_reset_trimmed_mean,
.free = grouping_free_trimmed_mean,
.add = grouping_add_trimmed_mean,
.flush = grouping_flush_trimmed_mean,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean5",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN5,
.init = NULL,
.create= grouping_create_trimmed_mean5,
.reset = grouping_reset_trimmed_mean,
.free = grouping_free_trimmed_mean,
.add = grouping_add_trimmed_mean,
.flush = grouping_flush_trimmed_mean,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean10",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN10,
.init = NULL,
.create= grouping_create_trimmed_mean10,
.reset = grouping_reset_trimmed_mean,
.free = grouping_free_trimmed_mean,
.add = grouping_add_trimmed_mean,
.flush = grouping_flush_trimmed_mean,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean15",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN15,
.init = NULL,
.create= grouping_create_trimmed_mean15,
.reset = grouping_reset_trimmed_mean,
.free = grouping_free_trimmed_mean,
.add = grouping_add_trimmed_mean,
.flush = grouping_flush_trimmed_mean,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean20",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN20,
.init = NULL,
.create= grouping_create_trimmed_mean20,
.reset = grouping_reset_trimmed_mean,
.free = grouping_free_trimmed_mean,
.add = grouping_add_trimmed_mean,
.flush = grouping_flush_trimmed_mean,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean25",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN25,
.init = NULL,
.create= grouping_create_trimmed_mean25,
.reset = grouping_reset_trimmed_mean,
.free = grouping_free_trimmed_mean,
.add = grouping_add_trimmed_mean,
.flush = grouping_flush_trimmed_mean,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-mean",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEAN5,
.init = NULL,
.create= grouping_create_trimmed_mean5,
.reset = grouping_reset_trimmed_mean,
.free = grouping_free_trimmed_mean,
.add = grouping_add_trimmed_mean,
.flush = grouping_flush_trimmed_mean,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "incremental_sum",
.hash = 0,
.value = RRDR_GROUPING_INCREMENTAL_SUM,
.init = NULL,
.create= grouping_create_incremental_sum,
.reset = grouping_reset_incremental_sum,
.free = grouping_free_incremental_sum,
.add = grouping_add_incremental_sum,
.flush = grouping_flush_incremental_sum,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "incremental-sum",
.hash = 0,
.value = RRDR_GROUPING_INCREMENTAL_SUM,
.init = NULL,
.create= grouping_create_incremental_sum,
.reset = grouping_reset_incremental_sum,
.free = grouping_free_incremental_sum,
.add = grouping_add_incremental_sum,
.flush = grouping_flush_incremental_sum,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "median",
.hash = 0,
.value = RRDR_GROUPING_MEDIAN,
.init = NULL,
.create= grouping_create_median,
.reset = grouping_reset_median,
.free = grouping_free_median,
.add = grouping_add_median,
.flush = grouping_flush_median,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median1",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN1,
.init = NULL,
.create= grouping_create_trimmed_median1,
.reset = grouping_reset_median,
.free = grouping_free_median,
.add = grouping_add_median,
.flush = grouping_flush_median,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median2",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN2,
.init = NULL,
.create= grouping_create_trimmed_median2,
.reset = grouping_reset_median,
.free = grouping_free_median,
.add = grouping_add_median,
.flush = grouping_flush_median,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median3",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN3,
.init = NULL,
.create= grouping_create_trimmed_median3,
.reset = grouping_reset_median,
.free = grouping_free_median,
.add = grouping_add_median,
.flush = grouping_flush_median,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median5",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN5,
.init = NULL,
.create= grouping_create_trimmed_median5,
.reset = grouping_reset_median,
.free = grouping_free_median,
.add = grouping_add_median,
.flush = grouping_flush_median,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median10",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN10,
.init = NULL,
.create= grouping_create_trimmed_median10,
.reset = grouping_reset_median,
.free = grouping_free_median,
.add = grouping_add_median,
.flush = grouping_flush_median,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median15",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN15,
.init = NULL,
.create= grouping_create_trimmed_median15,
.reset = grouping_reset_median,
.free = grouping_free_median,
.add = grouping_add_median,
.flush = grouping_flush_median,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median20",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN20,
.init = NULL,
.create= grouping_create_trimmed_median20,
.reset = grouping_reset_median,
.free = grouping_free_median,
.add = grouping_add_median,
.flush = grouping_flush_median,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median25",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN25,
.init = NULL,
.create= grouping_create_trimmed_median25,
.reset = grouping_reset_median,
.free = grouping_free_median,
.add = grouping_add_median,
.flush = grouping_flush_median,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "trimmed-median",
.hash = 0,
.value = RRDR_GROUPING_TRIMMED_MEDIAN5,
.init = NULL,
.create= grouping_create_trimmed_median5,
.reset = grouping_reset_median,
.free = grouping_free_median,
.add = grouping_add_median,
.flush = grouping_flush_median,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile25",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE25,
.init = NULL,
.create= grouping_create_percentile25,
.reset = grouping_reset_percentile,
.free = grouping_free_percentile,
.add = grouping_add_percentile,
.flush = grouping_flush_percentile,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile50",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE50,
.init = NULL,
.create= grouping_create_percentile50,
.reset = grouping_reset_percentile,
.free = grouping_free_percentile,
.add = grouping_add_percentile,
.flush = grouping_flush_percentile,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile75",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE75,
.init = NULL,
.create= grouping_create_percentile75,
.reset = grouping_reset_percentile,
.free = grouping_free_percentile,
.add = grouping_add_percentile,
.flush = grouping_flush_percentile,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile80",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE80,
.init = NULL,
.create= grouping_create_percentile80,
.reset = grouping_reset_percentile,
.free = grouping_free_percentile,
.add = grouping_add_percentile,
.flush = grouping_flush_percentile,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile90",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE90,
.init = NULL,
.create= grouping_create_percentile90,
.reset = grouping_reset_percentile,
.free = grouping_free_percentile,
.add = grouping_add_percentile,
.flush = grouping_flush_percentile,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile95",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE95,
.init = NULL,
.create= grouping_create_percentile95,
.reset = grouping_reset_percentile,
.free = grouping_free_percentile,
.add = grouping_add_percentile,
.flush = grouping_flush_percentile,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile97",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE97,
.init = NULL,
.create= grouping_create_percentile97,
.reset = grouping_reset_percentile,
.free = grouping_free_percentile,
.add = grouping_add_percentile,
.flush = grouping_flush_percentile,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile98",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE98,
.init = NULL,
.create= grouping_create_percentile98,
.reset = grouping_reset_percentile,
.free = grouping_free_percentile,
.add = grouping_add_percentile,
.flush = grouping_flush_percentile,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile99",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE99,
.init = NULL,
.create= grouping_create_percentile99,
.reset = grouping_reset_percentile,
.free = grouping_free_percentile,
.add = grouping_add_percentile,
.flush = grouping_flush_percentile,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "percentile",
.hash = 0,
.value = RRDR_GROUPING_PERCENTILE95,
.init = NULL,
.create= grouping_create_percentile95,
.reset = grouping_reset_percentile,
.free = grouping_free_percentile,
.add = grouping_add_percentile,
.flush = grouping_flush_percentile,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "min",
.hash = 0,
.value = RRDR_GROUPING_MIN,
.init = NULL,
.create= grouping_create_min,
.reset = grouping_reset_min,
.free = grouping_free_min,
.add = grouping_add_min,
.flush = grouping_flush_min,
.tier_query_fetch = TIER_QUERY_FETCH_MIN
},
{.name = "max",
.hash = 0,
.value = RRDR_GROUPING_MAX,
.init = NULL,
.create= grouping_create_max,
.reset = grouping_reset_max,
.free = grouping_free_max,
.add = grouping_add_max,
.flush = grouping_flush_max,
.tier_query_fetch = TIER_QUERY_FETCH_MAX
},
{.name = "sum",
.hash = 0,
.value = RRDR_GROUPING_SUM,
.init = NULL,
.create= grouping_create_sum,
.reset = grouping_reset_sum,
.free = grouping_free_sum,
.add = grouping_add_sum,
.flush = grouping_flush_sum,
.tier_query_fetch = TIER_QUERY_FETCH_SUM
},
// standard deviation
{.name = "stddev",
.hash = 0,
.value = RRDR_GROUPING_STDDEV,
.init = NULL,
.create= grouping_create_stddev,
.reset = grouping_reset_stddev,
.free = grouping_free_stddev,
.add = grouping_add_stddev,
.flush = grouping_flush_stddev,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "cv", // coefficient variation is calculated by stddev
.hash = 0,
.value = RRDR_GROUPING_CV,
.init = NULL,
.create= grouping_create_stddev, // not an error, stddev calculates this too
.reset = grouping_reset_stddev, // not an error, stddev calculates this too
.free = grouping_free_stddev, // not an error, stddev calculates this too
.add = grouping_add_stddev, // not an error, stddev calculates this too
.flush = grouping_flush_coefficient_of_variation,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "rsd", // alias of 'cv'
.hash = 0,
.value = RRDR_GROUPING_CV,
.init = NULL,
.create= grouping_create_stddev, // not an error, stddev calculates this too
.reset = grouping_reset_stddev, // not an error, stddev calculates this too
.free = grouping_free_stddev, // not an error, stddev calculates this too
.add = grouping_add_stddev, // not an error, stddev calculates this too
.flush = grouping_flush_coefficient_of_variation,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
/*
{.name = "mean", // same as average, no need to define it again
.hash = 0,
.value = RRDR_GROUPING_MEAN,
.setup = NULL,
.create= grouping_create_stddev,
.reset = grouping_reset_stddev,
.free = grouping_free_stddev,
.add = grouping_add_stddev,
.flush = grouping_flush_mean,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
*/
/*
{.name = "variance", // meaningless to offer
.hash = 0,
.value = RRDR_GROUPING_VARIANCE,
.setup = NULL,
.create= grouping_create_stddev,
.reset = grouping_reset_stddev,
.free = grouping_free_stddev,
.add = grouping_add_stddev,
.flush = grouping_flush_variance,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
*/
// single exponential smoothing
{.name = "ses",
.hash = 0,
.value = RRDR_GROUPING_SES,
.init = grouping_init_ses,
.create= grouping_create_ses,
.reset = grouping_reset_ses,
.free = grouping_free_ses,
.add = grouping_add_ses,
.flush = grouping_flush_ses,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "ema", // alias for 'ses'
.hash = 0,
.value = RRDR_GROUPING_SES,
.init = NULL,
.create= grouping_create_ses,
.reset = grouping_reset_ses,
.free = grouping_free_ses,
.add = grouping_add_ses,
.flush = grouping_flush_ses,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "ewma", // alias for ses
.hash = 0,
.value = RRDR_GROUPING_SES,
.init = NULL,
.create= grouping_create_ses,
.reset = grouping_reset_ses,
.free = grouping_free_ses,
.add = grouping_add_ses,
.flush = grouping_flush_ses,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
// double exponential smoothing
{.name = "des",
.hash = 0,
.value = RRDR_GROUPING_DES,
.init = grouping_init_des,
.create= grouping_create_des,
.reset = grouping_reset_des,
.free = grouping_free_des,
.add = grouping_add_des,
.flush = grouping_flush_des,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
{.name = "countif",
.hash = 0,
.value = RRDR_GROUPING_COUNTIF,
.init = NULL,
.create= grouping_create_countif,
.reset = grouping_reset_countif,
.free = grouping_free_countif,
.add = grouping_add_countif,
.flush = grouping_flush_countif,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
},
// terminator
{.name = NULL,
.hash = 0,
.value = RRDR_GROUPING_UNDEFINED,
.init = NULL,
.create= grouping_create_average,
.reset = grouping_reset_average,
.free = grouping_free_average,
.add = grouping_add_average,
.flush = grouping_flush_average,
.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE
}
};
void web_client_api_v1_init_grouping(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 *group_method2string(RRDR_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 "unknown-group-method";
}
RRDR_GROUPING web_client_api_request_v1_data_group(const char *name, RRDR_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 *web_client_api_request_v1_data_group_to_string(RRDR_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_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->internal.grouping_create = api_v1_data_groups[i].create;
r->internal.grouping_reset = api_v1_data_groups[i].reset;
r->internal.grouping_free = api_v1_data_groups[i].free;
r->internal.grouping_add = api_v1_data_groups[i].add;
r->internal.grouping_flush = api_v1_data_groups[i].flush;
r->internal.tier_query_fetch = api_v1_data_groups[i].tier_query_fetch;
found = 1;
}
}
if(!found) {
errno = 0;
internal_error(true, "QUERY: grouping method %u not found. Using 'average'", (unsigned int)group_method);
r->internal.grouping_create = grouping_create_average;
r->internal.grouping_reset = grouping_reset_average;
r->internal.grouping_free = grouping_free_average;
r->internal.grouping_add = grouping_add_average;
r->internal.grouping_flush = grouping_flush_average;
r->internal.tier_query_fetch = TIER_QUERY_FETCH_AVERAGE;
}
}
// ----------------------------------------------------------------------------
static void rrdr_disable_not_selected_dimensions(RRDR *r, RRDR_OPTIONS options, const char *dims,
struct context_param *context_param_list)
{
RRDDIM *temp_rd = context_param_list ? context_param_list->rd : NULL;
int should_lock = (!context_param_list || !(context_param_list->flags & CONTEXT_FLAGS_ARCHIVE));
if(unlikely(!dims || !*dims || (dims[0] == '*' && dims[1] == '\0'))) return;
if (should_lock)
rrdset_check_rdlock(r->st);
int match_ids = 0, match_names = 0;
if(unlikely(options & RRDR_OPTION_MATCH_IDS))
match_ids = 1;
if(unlikely(options & RRDR_OPTION_MATCH_NAMES))
match_names = 1;
if(likely(!match_ids && !match_names))
match_ids = match_names = 1;
SIMPLE_PATTERN *pattern = simple_pattern_create(dims, ",|\t\r\n\f\v", SIMPLE_PATTERN_EXACT);
RRDDIM *d;
long c, dims_selected = 0, dims_not_hidden_not_zero = 0;
for(c = 0, d = temp_rd?temp_rd:r->st->dimensions; d ;c++, d = d->next) {
if( (match_ids && simple_pattern_matches(pattern, rrddim_id(d)))
|| (match_names && simple_pattern_matches(pattern, rrddim_name(d)))
) {
r->od[c] |= RRDR_DIMENSION_SELECTED;
if(unlikely(r->od[c] & RRDR_DIMENSION_HIDDEN)) r->od[c] &= ~RRDR_DIMENSION_HIDDEN;
dims_selected++;
// since the user needs this dimension
// make it appear as NONZERO, to return it
// even if the dimension has only zeros
// unless option non_zero is set
if(unlikely(!(options & RRDR_OPTION_NONZERO)))
r->od[c] |= RRDR_DIMENSION_NONZERO;
// count the visible dimensions
if(likely(r->od[c] & RRDR_DIMENSION_NONZERO))
dims_not_hidden_not_zero++;
}
else {
r->od[c] |= RRDR_DIMENSION_HIDDEN;
if(unlikely(r->od[c] & RRDR_DIMENSION_SELECTED)) r->od[c] &= ~RRDR_DIMENSION_SELECTED;
}
}
simple_pattern_free(pattern);
// check if all dimensions are hidden
if(unlikely(!dims_not_hidden_not_zero && dims_selected)) {
// there are a few selected dimensions,
// but they are all zero
// enable the selected ones
// to avoid returning an empty chart
for(c = 0, d = temp_rd?temp_rd:r->st->dimensions; d ;c++, d = d->next)
if(unlikely(r->od[c] & RRDR_DIMENSION_SELECTED))
r->od[c] |= RRDR_DIMENSION_NONZERO;
}
}
// ----------------------------------------------------------------------------
// 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, time_t t, long rrdr_line) {
rrdr_line++;
internal_error(rrdr_line >= r->n,
"QUERY: requested to step above RRDR size for chart '%s'",
rrdset_name(r->st));
internal_error(r->t[rrdr_line] != 0 && r->t[rrdr_line] != t,
"QUERY: overwriting the timestamp of RRDR line %zu from %zu to %zu, of chart '%s'",
(size_t)rrdr_line, (size_t)r->t[rrdr_line], (size_t)t, rrdset_name(r->st));
// save the time
r->t[rrdr_line] = t;
return rrdr_line;
}
static inline void rrdr_done(RRDR *r, long rrdr_line) {
r->rows = rrdr_line + 1;
}
// ----------------------------------------------------------------------------
// tier management
static int rrddim_find_best_tier_for_timeframe(RRDDIM *rd, time_t after_wanted, time_t before_wanted, long points_wanted) {
if(unlikely(storage_tiers < 2))
return 0;
if(unlikely(after_wanted == before_wanted || points_wanted <= 0 || !rd || !rd->rrdset)) {
if(!rd)
internal_error(true, "QUERY: NULL dimension - invalid params to tier calculation");
else
internal_error(true, "QUERY: chart '%s' dimension '%s' invalid params to tier calculation",
(rd->rrdset)?rrdset_name(rd->rrdset):"unknown", rrddim_name(rd));
return 0;
}
//BUFFER *wb = buffer_create(1000);
//buffer_sprintf(wb, "Best tier for chart '%s', dim '%s', from %ld to %ld (dur %ld, every %d), points %ld",
// rd->rrdset->name, rd->name, after_wanted, before_wanted, before_wanted - after_wanted, rd->update_every, points_wanted);
long weight[storage_tiers];
for(int tier = 0; tier < storage_tiers ; tier++) {
if(unlikely(!rd->tiers[tier])) {
internal_error(true, "QUERY: tier %d of chart '%s' dimension '%s' not initialized",
tier, rrdset_name(rd->rrdset), rrddim_name(rd));
// buffer_free(wb);
return 0;
}
time_t first_t = rd->tiers[tier]->query_ops.oldest_time(rd->tiers[tier]->db_metric_handle);
time_t last_t = rd->tiers[tier]->query_ops.latest_time(rd->tiers[tier]->db_metric_handle);
time_t common_after = MAX(first_t, after_wanted);
time_t common_before = MIN(last_t, before_wanted);
long time_coverage = (common_before - common_after) * 1000 / (before_wanted - after_wanted);
if(time_coverage < 0) time_coverage = 0;
int update_every = (int)rd->tiers[tier]->tier_grouping * (int)rd->update_every;
if(unlikely(update_every == 0)) {
internal_error(true, "QUERY: update_every of tier %d for chart '%s' dimension '%s' is zero. tg = %d, ue = %d",
tier, rrdset_name(rd->rrdset), rrddim_name(rd), rd->tiers[tier]->tier_grouping, rd->update_every);
// buffer_free(wb);
return 0;
}
long points_available = (before_wanted - after_wanted) / update_every;
long points_delta = points_available - points_wanted;
long points_coverage = (points_delta < 0) ? points_available * 1000 / points_wanted: 1000;
if(points_available <= 0)
weight[tier] = -LONG_MAX;
else
weight[tier] = points_coverage;
// buffer_sprintf(wb, ": tier %d, first %ld, last %ld (dur %ld, tg %d, every %d), points %ld, tcoverage %ld, pcoverage %ld, weight %ld",
// tier, first_t, last_t, last_t - first_t, rd->tiers[tier]->tier_grouping, update_every,
// points_available, time_coverage, points_coverage, weight[tier]);
}
int best_tier = 0;
for(int tier = 1; tier < storage_tiers ; tier++) {
if(weight[tier] >= weight[best_tier])
best_tier = tier;
}
if(weight[best_tier] == -LONG_MAX)
best_tier = 0;
//buffer_sprintf(wb, ": final best tier %d", best_tier);
//internal_error(true, "%s", buffer_tostring(wb));
//buffer_free(wb);
return best_tier;
}
static int rrdset_find_natural_update_every_for_timeframe(RRDSET *st, time_t after_wanted, time_t before_wanted, long points_wanted, RRDR_OPTIONS options, int tier) {
int ret = st->update_every;
if(unlikely(!rrdset_number_of_dimensions(st)))
return ret;
RRDDIM *first_rd = NULL;
rrddim_foreach_read(first_rd, st) break; rrddim_foreach_done(first_rd);
if(!first_rd)
return ret;
int best_tier;
if(options & RRDR_OPTION_SELECTED_TIER && tier >= 0 && tier < storage_tiers)
best_tier = tier;
else {
best_tier = rrddim_find_best_tier_for_timeframe(first_rd, after_wanted, before_wanted, points_wanted);
}
if(!first_rd->tiers[best_tier]) {
internal_error(
true,
"QUERY: tier %d on chart '%s', is not initialized", best_tier, rrdset_name(st));
}
else {
ret = (int)first_rd->tiers[best_tier]->tier_grouping * (int)st->update_every;
if(unlikely(!ret)) {
internal_error(
true,
"QUERY: update_every calculated to be zero on chart '%s', tier_grouping %d, update_every %d",
rrdset_name(st), first_rd->tiers[best_tier]->tier_grouping, st->update_every);
ret = st->update_every;
}
}
return ret;
}
// ----------------------------------------------------------------------------
// query ops
typedef struct query_point {
time_t end_time;
time_t start_time;
NETDATA_DOUBLE value;
NETDATA_DOUBLE anomaly;
SN_FLAGS flags;
#ifdef NETDATA_INTERNAL_CHECKS
size_t id;
#endif
} QUERY_POINT;
QUERY_POINT QUERY_POINT_EMPTY = {
.end_time = 0,
.start_time = 0,
.value = NAN,
.anomaly = 0,
.flags = SN_FLAG_NONE,
#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_plan_entry {
size_t tier;
time_t after;
time_t before;
} QUERY_PLAN_ENTRY;
typedef struct query_plan {
size_t entries;
QUERY_PLAN_ENTRY data[RRD_STORAGE_TIERS*2];
} QUERY_PLAN;
typedef struct query_engine_ops {
// configuration
RRDR *r;
RRDDIM *rd;
time_t view_update_every;
time_t query_granularity;
TIER_QUERY_FETCH tier_query_fetch;
// query planer
QUERY_PLAN plan;
size_t current_plan;
time_t current_plan_expire_time;
// storage queries
size_t tier;
struct rrddim_tier *tier_ptr;
struct rrddim_query_handle handle;
STORAGE_POINT (*next_metric)(struct rrddim_query_handle *handle);
int (*is_finished)(struct rrddim_query_handle *handle);
void (*finalize)(struct rrddim_query_handle *handle);
// aggregating points over time
void (*grouping_add)(struct rrdresult *r, NETDATA_DOUBLE value);
NETDATA_DOUBLE (*grouping_flush)(struct rrdresult *r, RRDR_VALUE_FLAGS *rrdr_value_options_ptr);
size_t group_points_non_zero;
size_t group_points_added;
NETDATA_DOUBLE group_anomaly_rate;
RRDR_VALUE_FLAGS group_value_flags;
// statistics
size_t db_total_points_read;
size_t db_points_read_per_tier[RRD_STORAGE_TIERS];
} QUERY_ENGINE_OPS;
// ----------------------------------------------------------------------------
// query planer
#define query_plan_should_switch_plan(ops, now) ((now) >= (ops).current_plan_expire_time)
static void query_planer_activate_plan(QUERY_ENGINE_OPS *ops, size_t plan_id, time_t overwrite_after) {
if(unlikely(plan_id >= ops->plan.entries))
plan_id = ops->plan.entries - 1;
time_t after = ops->plan.data[plan_id].after;
time_t before = ops->plan.data[plan_id].before;
if(overwrite_after > after && overwrite_after < before)
after = overwrite_after;
ops->tier = ops->plan.data[plan_id].tier;
ops->tier_ptr = ops->rd->tiers[ops->tier];
ops->tier_ptr->query_ops.init(ops->tier_ptr->db_metric_handle, &ops->handle, after, before, ops->r->internal.tier_query_fetch);
ops->next_metric = ops->tier_ptr->query_ops.next_metric;
ops->is_finished = ops->tier_ptr->query_ops.is_finished;
ops->finalize = ops->tier_ptr->query_ops.finalize;
ops->current_plan = plan_id;
ops->current_plan_expire_time = ops->plan.data[plan_id].before;
}
static void query_planer_next_plan(QUERY_ENGINE_OPS *ops, time_t now, time_t last_point_end_time) {
internal_error(now < ops->current_plan_expire_time && now < ops->plan.data[ops->current_plan].before,
"QUERY: switching query plan too early!");
time_t next_plan_before_time;
do {
ops->current_plan++;
if (ops->current_plan >= ops->plan.entries) {
ops->current_plan = ops->plan.entries - 1;
return;
}
next_plan_before_time = ops->plan.data[ops->current_plan].before;
} while(now >= next_plan_before_time || last_point_end_time >= next_plan_before_time);
if(ops->finalize) {
ops->finalize(&ops->handle);
ops->finalize = NULL;
}
query_planer_activate_plan(ops, ops->current_plan, MIN(now, last_point_end_time));
// internal_error(true, "QUERY: switched plan to %zu (all is %zu), previous expiration was %ld, this starts at %ld, now is %ld, last_point_end_time %ld", ops->current_plan, ops->plan.entries, ops->plan.data[ops->current_plan-1].before, ops->plan.data[ops->current_plan].after, now, last_point_end_time);
}
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 void query_plan(QUERY_ENGINE_OPS *ops, time_t after_wanted, time_t before_wanted, long points_wanted) {
RRDDIM *rd = ops->rd;
//BUFFER *wb = buffer_create(1000);
//buffer_sprintf(wb, "QUERY PLAN for chart '%s' dimension '%s', from %ld to %ld:", rd->rrdset->name, rd->name, after_wanted, before_wanted);
// put our selected tier as the first plan
size_t selected_tier;
if(ops->r->internal.query_options & RRDR_OPTION_SELECTED_TIER && ops->r->internal.query_tier >= 0 && ops->r->internal.query_tier < storage_tiers) {
selected_tier = ops->r->internal.query_tier;
}
else {
selected_tier = rrddim_find_best_tier_for_timeframe(rd, after_wanted, before_wanted, points_wanted);
if(ops->r->internal.query_options & RRDR_OPTION_SELECTED_TIER)
ops->r->internal.query_options &= ~RRDR_OPTION_SELECTED_TIER;
}
ops->plan.entries = 1;
ops->plan.data[0].tier = selected_tier;
ops->plan.data[0].after = rd->tiers[selected_tier]->query_ops.oldest_time(rd->tiers[selected_tier]->db_metric_handle);
ops->plan.data[0].before = rd->tiers[selected_tier]->query_ops.latest_time(rd->tiers[selected_tier]->db_metric_handle);
if(!(ops->r->internal.query_options & RRDR_OPTION_SELECTED_TIER)) {
// the selected tier
time_t selected_tier_first_time_t = ops->plan.data[0].after;
time_t selected_tier_last_time_t = ops->plan.data[0].before;
//buffer_sprintf(wb, ": SELECTED tier %zu, from %ld to %ld", selected_tier, ops->plan.data[0].after, ops->plan.data[0].before);
// check if our selected tier can start the query
if (selected_tier_first_time_t > after_wanted) {
// we need some help from other tiers
for (int tr = (int)selected_tier + 1; tr < storage_tiers; tr++) {
// find the first time of this tier
time_t first_time_t = rd->tiers[tr]->query_ops.oldest_time(rd->tiers[tr]->db_metric_handle);
//buffer_sprintf(wb, ": EVAL AFTER tier %d, %ld", tier, first_time_t);
// can it help?
if (first_time_t < selected_tier_first_time_t) {
// it can help us add detail at the beginning of the query
QUERY_PLAN_ENTRY t = {
.tier = tr,
.after = (first_time_t < after_wanted) ? after_wanted : first_time_t,
.before = selected_tier_first_time_t};
ops->plan.data[ops->plan.entries++] = t;
// prepare for the tier
selected_tier_first_time_t = t.after;
if (t.after <= after_wanted)
break;
}
}
}
// check if our selected tier can finish the query
if (selected_tier_last_time_t < before_wanted) {
// we need some help from other tiers
for (int tr = (int)selected_tier - 1; tr >= 0; tr--) {
// find the last time of this tier
time_t last_time_t = rd->tiers[tr]->query_ops.latest_time(rd->tiers[tr]->db_metric_handle);
//buffer_sprintf(wb, ": EVAL BEFORE tier %d, %ld", tier, last_time_t);
// can it help?
if (last_time_t > selected_tier_last_time_t) {
// it can help us add detail at the end of the query
QUERY_PLAN_ENTRY t = {
.tier = tr,
.after = selected_tier_last_time_t,
.before = (last_time_t > before_wanted) ? before_wanted : last_time_t};
ops->plan.data[ops->plan.entries++] = t;
// prepare for the tier
selected_tier_last_time_t = t.before;
if (t.before >= before_wanted)
break;
}
}
}
}
// sort the query plan
if(ops->plan.entries > 1)
qsort(&ops->plan.data, ops->plan.entries, sizeof(QUERY_PLAN_ENTRY), compare_query_plan_entries_on_start_time);
// make sure it has the whole timeframe we need
ops->plan.data[0].after = after_wanted;
ops->plan.data[ops->plan.entries - 1].before = before_wanted;
//buffer_sprintf(wb, ": FINAL STEPS %zu", ops->plan.entries);
//for(size_t i = 0; i < ops->plan.entries ;i++)
// buffer_sprintf(wb, ": STEP %zu = use tier %zu from %ld to %ld", i+1, ops->plan.data[i].tier, ops->plan.data[i].after, ops->plan.data[i].before);
//internal_error(true, "%s", buffer_tostring(wb));
query_planer_activate_plan(ops, 0, 0);
}
// ----------------------------------------------------------------------------
// 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).end_time - (this_point).start_time > 1 \
\
/* the two points are exactly next to each other */ \
&& (last_point).end_time == (this_point).start_time \
\
/* 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).end_time - (now)) / (NETDATA_DOUBLE)((this_point).end_time - (this_point).start_time)); \
(this_point).end_time = now; \
} \
} while(0)
#define query_add_point_to_group(r, point, ops) do { \
if(likely(netdata_double_isnumber((point).value))) { \
if(likely(fpclassify((point).value) != FP_ZERO)) \
(ops).group_points_non_zero++; \
\
if(unlikely((point).flags & SN_FLAG_RESET)) \
(ops).group_value_flags |= RRDR_VALUE_RESET; \
\
(ops).grouping_add(r, (point).value); \
} \
\
(ops).group_points_added++; \
(ops).group_anomaly_rate += (point).anomaly; \
} while(0)
static inline void rrd2rrdr_do_dimension(
RRDR *r
, long points_wanted
, RRDDIM *rd
, long dim_id_in_rrdr
, time_t after_wanted
, time_t before_wanted
){
time_t max_date = 0,
min_date = 0;
size_t points_added = 0;
QUERY_ENGINE_OPS ops = {
.r = r,
.rd = rd,
.grouping_add = r->internal.grouping_add,
.grouping_flush = r->internal.grouping_flush,
.tier_query_fetch = r->internal.tier_query_fetch,
.view_update_every = r->update_every,
.query_granularity = r->update_every / r->group,
.group_value_flags = RRDR_VALUE_NOTHING
};
long rrdr_line = -1;
bool use_anomaly_bit_as_value = (r->internal.query_options & RRDR_OPTION_ANOMALY_BIT) ? true : false;
query_plan(&ops, after_wanted, before_wanted, points_wanted);
NETDATA_DOUBLE min = r->min, max = r->max;
QUERY_POINT last2_point = QUERY_POINT_EMPTY;
QUERY_POINT last1_point = QUERY_POINT_EMPTY;
QUERY_POINT new_point = QUERY_POINT_EMPTY;
time_t now_start_time = after_wanted - ops.query_granularity;
time_t now_end_time = after_wanted + ops.view_update_every - ops.query_granularity;
// The main loop, based on the query granularity we need
for( ; (long)points_added < points_wanted ; now_start_time = now_end_time, now_end_time += ops.view_update_every) {
if(query_plan_should_switch_plan(ops, now_end_time))
query_planer_next_plan(&ops, now_end_time, new_point.end_time);
// 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(ops.is_finished(&ops.handle))) {
if(count_same_end_time != 0) {
last2_point = last1_point;
last1_point = new_point;
}
new_point = QUERY_POINT_EMPTY;
new_point.start_time = last1_point.end_time;
new_point.end_time = now_end_time;
break;
}
// fetch the new point
{
STORAGE_POINT sp = ops.next_metric(&ops.handle);
ops.db_points_read_per_tier[ops.tier]++;
ops.db_total_points_read++;
new_point.start_time = sp.start_time;
new_point.end_time = sp.end_time;
new_point.anomaly = sp.count ? (NETDATA_DOUBLE)sp.anomaly_count * 100.0 / (NETDATA_DOUBLE)sp.count : 0.0;
query_point_set_id(new_point, ops.db_total_points_read);
// set the right value to the point we got
if(likely(!storage_point_is_unset(sp) && !storage_point_is_empty(sp))) {
if(unlikely(use_anomaly_bit_as_value))
new_point.value = new_point.anomaly;
else {
switch (ops.tier_query_fetch) {
default:
case TIER_QUERY_FETCH_AVERAGE:
new_point.value = sp.sum / 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;
new_point.flags = SN_FLAG_NONE;
}
}
// check if the db is giving us zero duration points
if(unlikely(new_point.start_time == new_point.end_time)) {
internal_error(true, "QUERY: next_metric(%s, %s) returned point %zu start time %ld, end time %ld, that are both equal",
rrdset_name(rd->rrdset), rrddim_name(rd), new_point.id, new_point.start_time, new_point.end_time);
new_point.start_time = new_point.end_time - ((time_t)ops.tier_ptr->tier_grouping * (time_t)ops.rd->update_every);
}
// check if the db is advancing the query
if(unlikely(new_point.end_time <= last1_point.end_time)) {
internal_error(true, "QUERY: next_metric(%s, %s) returned point %zu from %ld time %ld, before the last point %zu end time %ld, now is %ld to %ld",
rrdset_name(rd->rrdset), rrddim_name(rd), new_point.id, new_point.start_time, new_point.end_time,
last1_point.id, last1_point.end_time, 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.end_time < now_end_time)) { // likely to favor tier0
// this db point ends before our now_end_time
if(likely(new_point.end_time >= 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);
}
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.end_time < after_wanted && new_point.id > 1,
"QUERY: next_metric(%s, %s) 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)",
rrdset_name(rd->rrdset), rrddim_name(rd),
new_point.id, new_point.start_time, new_point.end_time,
now_start_time, now_end_time,
after_wanted, before_wanted);
}
}
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: 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",
last1_point.end_time, count_same_end_time);
if(unlikely(new_point.end_time <= last1_point.end_time))
new_point.end_time = now_end_time;
}
// the inner loop
// we have 3 points in memory: last2, last1, new
// we select the one to use based on their timestamps
size_t iterations = 0;
for ( ; now_end_time <= new_point.end_time && (long)points_added < points_wanted ;
now_end_time += ops.view_update_every, iterations++) {
// 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.start_time)) {
// it is time for our NEW point to be used
current_point = new_point;
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: on '%s', dim '%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.",
rrdset_name(rd->rrdset), rrddim_name(rd), 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.end_time)) {
// our LAST point is still valid
current_point = last1_point;
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: on '%s', dim '%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.",
rrdset_name(rd->rrdset), rrddim_name(rd), 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);
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;
if(unlikely(!min_date)) min_date = now_end_time;
max_date = now_end_time;
// 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 = ops.grouping_flush(r, rrdr_value_options_ptr);
r->v[rrdr_o_v_index] = group_value;
// we only store uint8_t anomaly rates,
// so let's get double precision by storing
// anomaly rates in the range 0 - 200
r->ar[rrdr_o_v_index] = ops.group_anomaly_rate / (NETDATA_DOUBLE)ops.group_points_added;
if(likely(points_added || dim_id_in_rrdr)) {
// 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 dim_id_in_rrdr == 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_anomaly_rate = 0;
}
// the loop above increased "now" by query_granularity,
// but the main loop will increase it too,
// so, let's undo the last iteration of this loop
if(iterations)
now_end_time -= ops.view_update_every;
}
ops.finalize(&ops.handle);
r->internal.result_points_generated += points_added;
r->internal.db_points_read += ops.db_total_points_read;
for(int tr = 0; tr < storage_tiers ; tr++)
r->internal.tier_points_read[tr] += ops.db_points_read_per_tier[tr];
r->min = min;
r->max = max;
r->before = max_date;
r->after = min_date - ops.view_update_every + ops.query_granularity;
rrdr_done(r, rrdr_line);
internal_error((long)points_added != points_wanted,
"QUERY: query on %s/%s requested %zu points, but RRDR added %zu (%zu db points read).",
rrdset_name(r->st), rrddim_name(rd), (size_t)points_wanted, (size_t)points_added, ops.db_total_points_read);
}
// ----------------------------------------------------------------------------
// fill the gap of a tier
extern void store_metric_at_tier(RRDDIM *rd, struct rrddim_tier *t, STORAGE_POINT sp, usec_t now_ut);
void rrdr_fill_tier_gap_from_smaller_tiers(RRDDIM *rd, int tier, time_t now) {
if(unlikely(tier < 0 || tier >= storage_tiers)) return;
if(storage_tiers_backfill[tier] == RRD_BACKFILL_NONE) return;
struct rrddim_tier *t = rd->tiers[tier];
if(unlikely(!t)) return;
time_t latest_time_t = t->query_ops.latest_time(t->db_metric_handle);
time_t granularity = (time_t)t->tier_grouping * (time_t)rd->update_every;
time_t time_diff = now - latest_time_t;
// if the user wants only NEW backfilling, and we don't have any data
if(storage_tiers_backfill[tier] == RRD_BACKFILL_NEW && latest_time_t <= 0) return;
// there is really nothing we can do
if(now <= latest_time_t || time_diff < granularity) return;
struct rrddim_query_handle handle;
size_t all_points_read = 0;
// for each lower tier
for(int tr = tier - 1; tr >= 0 ;tr--){
time_t smaller_tier_first_time = rd->tiers[tr]->query_ops.oldest_time(rd->tiers[tr]->db_metric_handle);
time_t smaller_tier_last_time = rd->tiers[tr]->query_ops.latest_time(rd->tiers[tr]->db_metric_handle);
if(smaller_tier_last_time <= latest_time_t) continue; // it is as bad as we are
long after_wanted = (latest_time_t < smaller_tier_first_time) ? smaller_tier_first_time : latest_time_t;
long before_wanted = smaller_tier_last_time;
struct rrddim_tier *tmp = rd->tiers[tr];
tmp->query_ops.init(tmp->db_metric_handle, &handle, after_wanted, before_wanted, TIER_QUERY_FETCH_AVERAGE);
size_t points = 0;
while(!tmp->query_ops.is_finished(&handle)) {
STORAGE_POINT sp = tmp->query_ops.next_metric(&handle);
if(sp.end_time > latest_time_t) {
latest_time_t = sp.end_time;
store_metric_at_tier(rd, t, sp, sp.end_time * USEC_PER_SEC);
points++;
}
}
all_points_read += points;
tmp->query_ops.finalize(&handle);
//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);
}
rrdr_query_completed(all_points_read, all_points_read);
}
// ----------------------------------------------------------------------------
// 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_GROUPING group_method
, bool aligned
, long group
, long resampling_time
, long resampling_group
, time_t after_wanted
, time_t after_requested
, time_t before_wanted
, time_t before_requested
, long points_requested
, long points_wanted
//, size_t after_slot
//, size_t before_slot
, const char *msg
) {
time_t first_entry_t = rrdset_first_entry_t(r->st);
time_t last_entry_t = rrdset_last_entry_t(r->st);
info("INTERNAL ERROR: rrd2rrdr() on %s update every %d with %s grouping %s (group: %ld, resampling_time: %ld, resampling_group: %ld), "
"after (got: %zu, want: %zu, req: %ld, db: %zu), "
"before (got: %zu, want: %zu, req: %ld, db: %zu), "
"duration (got: %zu, want: %zu, req: %ld, db: %zu), "
//"slot (after: %zu, before: %zu, delta: %zu), "
"points (got: %ld, want: %ld, req: %ld, db: %ld), "
"%s"
, rrdset_name(r->st)
, r->st->update_every
// grouping
, (aligned) ? "aligned" : "unaligned"
, group_method2string(group_method)
, group
, resampling_time
, resampling_group
// after
, (size_t)r->after
, (size_t)after_wanted
, after_requested
, (size_t)first_entry_t
// before
, (size_t)r->before
, (size_t)before_wanted
, before_requested
, (size_t)last_entry_t
// duration
, (size_t)(r->before - r->after + r->st->update_every)
, (size_t)(before_wanted - after_wanted + r->st->update_every)
, before_requested - after_requested
, (size_t)((last_entry_t - first_entry_t) + r->st->update_every)
// slot
/*
, after_slot
, before_slot
, (after_slot > before_slot) ? (r->st->entries - after_slot + before_slot) : (before_slot - after_slot)
*/
// points
, r->rows
, points_wanted
, points_requested
, r->st->entries
// message
, msg
);
}
#endif // NETDATA_INTERNAL_CHECKS
// Returns 1 if an absolute period was requested or 0 if it was a relative period
int rrdr_relative_window_to_absolute(long long *after, long long *before) {
time_t now = now_realtime_sec() - 1;
int absolute_period_requested = -1;
long long after_requested, before_requested;
before_requested = *before;
after_requested = *after;
// allow relative for before (smaller than API_RELATIVE_TIME_MAX)
if(ABS(before_requested) <= API_RELATIVE_TIME_MAX) {
// if the user asked for a positive relative time,
// flip it to a negative
if(before_requested > 0)
before_requested = -before_requested;
before_requested = now + before_requested;
absolute_period_requested = 0;
}
// allow relative for after (smaller than API_RELATIVE_TIME_MAX)
if(ABS(after_requested) <= API_RELATIVE_TIME_MAX) {
if(after_requested > 0)
after_requested = -after_requested;
// if the user didn't give an after, use the number of points
// to give a sane default
if(after_requested == 0)
after_requested = -600;
// since the query engine now returns inclusive timestamps
// it is awkward to return 6 points when after=-5 is given
// so for relative queries we add 1 second, to give
// more predictable results to users.
after_requested = before_requested + after_requested + 1;
absolute_period_requested = 0;
}
if(absolute_period_requested == -1)
absolute_period_requested = 1;
// check if the parameters are flipped
if(after_requested > before_requested) {
long long t = before_requested;
before_requested = after_requested;
after_requested = t;
}
// if the query requests future data
// shift the query back to be in the present time
// (this may also happen because of the rules above)
if(before_requested > now) {
long long delta = before_requested - now;
before_requested -= delta;
after_requested -= delta;
}
*before = before_requested;
*after = after_requested;
return absolute_period_requested;
}
// #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() { \
info("QUERY: chart '%s', after:%lld, before:%lld, duration:%lld, points:%ld, res:%ld - wanted => after:%lld, before:%lld, points:%ld, group:%ld, granularity:%ld, resgroup:%ld, resdiv:" NETDATA_DOUBLE_FORMAT_AUTO " %s", st->name, 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
RRDR *rrd2rrdr(
ONEWAYALLOC *owa
, RRDSET *st
, long points_requested
, long long after_requested
, long long before_requested
, RRDR_GROUPING group_method
, long resampling_time_requested
, RRDR_OPTIONS options
, const char *dimensions
, struct context_param *context_param_list
, const char *group_options
, int timeout
, int tier
) {
// 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
long points_wanted = points_requested;
long long after_wanted = after_requested;
long long before_wanted = before_requested;
int update_every = st->update_every;
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();
// make sure points_wanted is positive
if(points_wanted < 0) {
points_wanted = -points_wanted;
query_debug_log(":-points_wanted %ld", points_wanted);
}
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) {
// for non-context queries we have to find the duration of the database
// for context queries we will assume 600 seconds duration
if(!context_param_list) {
relative_period_requested = true;
time_t first_entry_t = rrdset_first_entry_t(st);
time_t last_entry_t = rrdset_last_entry_t(st);
if(first_entry_t == 0 || last_entry_t == 0) {
internal_error(true, "QUERY: chart without data detected on '%s'", rrdset_name(st));
query_debug_log_free();
return NULL;
}
query_debug_log(":first_entry_t %ld, last_entry_t %ld", first_entry_t, last_entry_t);
if (after_wanted == 0) {
after_wanted = first_entry_t;
query_debug_log(":zero after_wanted %lld", after_wanted);
}
if (before_wanted == 0) {
before_wanted = last_entry_t;
before_is_aligned_to_db_end = true;
query_debug_log(":zero before_wanted %lld", before_wanted);
}
if(points_wanted == 0) {
points_wanted = (last_entry_t - first_entry_t) / update_every;
query_debug_log(":zero points_wanted %ld", points_wanted);
}
}
// if they are still zero, assume 600
if(after_wanted == 0) {
after_wanted = -600;
query_debug_log(":zero600 after_wanted %lld", after_wanted);
}
}
if(points_wanted == 0) {
points_wanted = 600;
query_debug_log(":zero600 points_wanted %ld", points_wanted);
}
// convert our before_wanted and after_wanted to absolute
rrdr_relative_window_to_absolute(&after_wanted, &before_wanted);
query_debug_log(":relative2absolute after %lld, before %lld", 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(st, after_wanted, before_wanted, points_wanted, options, tier);
if(update_every <= 0) update_every = st->update_every;
query_debug_log(":natural update every %d", 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 %lld", before_wanted);
}
if (after_wanted % query_granularity) {
after_wanted -= after_wanted % query_granularity;
query_debug_log(":granularity align after_wanted %lld", 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 %ld", 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 %lld", 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 %lld", after_wanted);
}
}
// the available points of the query
long points_available = (duration + 1) / query_granularity;
if(unlikely(points_available <= 0)) points_available = 1;
query_debug_log(":points_available %ld", points_available);
if(points_wanted > points_available) {
points_wanted = points_available;
query_debug_log(":max points_wanted %ld", points_wanted);
}
// calculate the desired grouping of source data points
long 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 %ld", group);
if(points_wanted * group * query_granularity < 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 %ld", points_wanted);
}
// resampling_time_requested enforces a certain grouping multiple
NETDATA_DOUBLE resampling_divisor = 1.0;
long 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 %ld", 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 %ld", 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 %ld", 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 % (group * query_granularity);
else
before_wanted += (group * query_granularity) - before_wanted % (group * query_granularity);
query_debug_log(":align before_wanted %lld", before_wanted);
}
after_wanted = before_wanted - (points_wanted * group * query_granularity) + query_granularity;
query_debug_log(":final after_wanted %lld", after_wanted);
duration = before_wanted - after_wanted;
query_debug_log(":final duration %ld", duration + 1);
// check the context query based on the starting time of the query
if (context_param_list && !(context_param_list->flags & CONTEXT_FLAGS_ARCHIVE)) {
rebuild_context_param_list(owa, context_param_list, after_wanted);
st = context_param_list->rd ? context_param_list->rd->rrdset : NULL;
if(unlikely(!st))
return NULL;
}
internal_error(points_wanted != duration / (query_granularity * group) + 1,
"QUERY: points_wanted %ld is not points %ld",
points_wanted, duration / (query_granularity * group) + 1);
internal_error(group < resampling_group,
"QUERY: group %ld is less than the desired group points %ld",
group, resampling_group);
internal_error(group > resampling_group && group % resampling_group,
"QUERY: group %ld is not a multiple of the desired group points %ld",
group, resampling_group);
// -------------------------------------------------------------------------
// initialize our result set
// this also locks the chart for us
RRDR *r = rrdr_create(owa, st, points_wanted, context_param_list);
if(unlikely(!r)) {
internal_error(true, "QUERY: cannot create RRDR for %s, after=%u, before=%u, duration=%u, points=%ld",
rrdset_id(st), (uint32_t)after_wanted, (uint32_t)before_wanted, (uint32_t)duration, points_wanted);
return NULL;
}
if(unlikely(!r->d || !points_wanted)) {
internal_error(true, "QUERY: returning empty RRDR (no dimensions in RRDSET) for %s, after=%u, before=%u, duration=%zu, points=%ld",
rrdset_id(st), (uint32_t)after_wanted, (uint32_t)before_wanted, (size_t)duration, points_wanted);
return r;
}
if(relative_period_requested)
r->result_options |= RRDR_RESULT_OPTION_RELATIVE;
else
r->result_options |= RRDR_RESULT_OPTION_ABSOLUTE;
// find how many dimensions we have
long dimensions_count = r->d;
// -------------------------------------------------------------------------
// initialize RRDR
r->group = group;
r->update_every = (int)(group * query_granularity);
r->before = before_wanted;
r->after = after_wanted;
r->internal.points_wanted = points_wanted;
r->internal.resampling_group = resampling_group;
r->internal.resampling_divisor = resampling_divisor;
r->internal.query_options = options;
r->internal.query_tier = tier;
// -------------------------------------------------------------------------
// assign the processor functions
rrdr_set_grouping_function(r, group_method);
// allocate any memory required by the grouping method
r->internal.grouping_create(r, group_options);
// -------------------------------------------------------------------------
// disable the not-wanted dimensions
if (context_param_list && !(context_param_list->flags & CONTEXT_FLAGS_ARCHIVE))
rrdset_check_rdlock(st);
if(dimensions && *dimensions)
rrdr_disable_not_selected_dimensions(r, options, dimensions, context_param_list);
query_debug_log_fin();
// -------------------------------------------------------------------------
// do the work for each dimension
time_t max_after = 0, min_before = 0;
long max_rows = 0;
RRDDIM *first_rd = context_param_list ? context_param_list->rd : st->dimensions;
RRDDIM *rd;
long c, dimensions_used = 0, dimensions_nonzero = 0;
struct timeval query_start_time;
struct timeval query_current_time;
if (timeout) now_realtime_timeval(&query_start_time);
for(rd = first_rd, c = 0 ; rd && c < dimensions_count ; rd = rd->next, c++) {
// if we need a percentage, we need to calculate all dimensions
if(unlikely(!(options & RRDR_OPTION_PERCENTAGE) && (r->od[c] & RRDR_DIMENSION_HIDDEN))) {
if(unlikely(r->od[c] & RRDR_DIMENSION_SELECTED)) r->od[c] &= ~RRDR_DIMENSION_SELECTED;
continue;
}
r->od[c] |= RRDR_DIMENSION_SELECTED;
// reset the grouping for the new dimension
r->internal.grouping_reset(r);
rrd2rrdr_do_dimension(r, points_wanted, rd, c, after_wanted, before_wanted);
if (timeout)
now_realtime_timeval(&query_current_time);
if(r->od[c] & RRDR_DIMENSION_NONZERO)
dimensions_nonzero++;
// verify all dimensions are aligned
if(unlikely(!dimensions_used)) {
min_before = r->before;
max_after = r->after;
max_rows = r->rows;
}
else {
if(r->after != max_after) {
internal_error(true, "QUERY: 'after' mismatch between dimensions for chart '%s': max is %zu, dimension '%s' has %zu",
rrdset_name(st), (size_t)max_after, rrddim_name(rd), (size_t)r->after);
r->after = (r->after > max_after) ? r->after : max_after;
}
if(r->before != min_before) {
internal_error(true, "QUERY: 'before' mismatch between dimensions for chart '%s': max is %zu, dimension '%s' has %zu",
rrdset_name(st), (size_t)min_before, rrddim_name(rd), (size_t)r->before);
r->before = (r->before < min_before) ? r->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",
rrdset_name(st), (size_t)max_rows, rrddim_name(rd), (size_t)r->rows);
r->rows = (r->rows > max_rows) ? r->rows : max_rows;
}
}
dimensions_used++;
if (timeout && ((NETDATA_DOUBLE)dt_usec(&query_start_time, &query_current_time) / 1000.0) > timeout) {
log_access("QUERY CANCELED RUNTIME EXCEEDED %0.2f ms (LIMIT %d ms)",
(NETDATA_DOUBLE)dt_usec(&query_start_time, &query_current_time) / 1000.0, timeout);
r->result_options |= RRDR_RESULT_OPTION_CANCEL;
break;
}
}
#ifdef NETDATA_INTERNAL_CHECKS
if (dimensions_used) {
if(r->internal.log)
rrd2rrdr_log_request_response_metadata(r, options, group_method, aligned, group, resampling_time_requested, resampling_group,
after_wanted, after_requested, before_wanted, before_requested,
points_requested, points_wanted, /*after_slot, before_slot,*/
r->internal.log);
if(r->rows != points_wanted)
rrd2rrdr_log_request_response_metadata(r, options, group_method, aligned, group, resampling_time_requested, resampling_group,
after_wanted, after_requested, before_wanted, before_requested,
points_requested, points_wanted, /*after_slot, before_slot,*/
"got 'points' is not wanted 'points'");
if(aligned && (r->before % (group * query_granularity)) != 0)
rrd2rrdr_log_request_response_metadata(r, options, group_method, aligned, group, resampling_time_requested, resampling_group,
after_wanted, after_requested, before_wanted,before_wanted,
points_requested, points_wanted, /*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(aligned && (r->after % group) != 0)
// rrd2rrdr_log_request_response_metadata(r, options, group_method, aligned, group, resampling_time_requested, resampling_group, after_wanted, after_requested, before_wanted, before_requested, points_requested, points_wanted, after_slot, before_slot, "'after' is not aligned but alignment is required");
if(r->before != before_wanted)
rrd2rrdr_log_request_response_metadata(r, options, group_method, aligned, group, resampling_time_requested, resampling_group,
after_wanted, after_requested, before_wanted, before_requested,
points_requested, points_wanted, /*after_slot, before_slot,*/
"chart is not aligned to requested 'before'");
if(r->before != before_wanted)
rrd2rrdr_log_request_response_metadata(r, options, group_method, aligned, group, resampling_time_requested, resampling_group,
after_wanted, after_requested, before_wanted, before_requested,
points_requested, points_wanted, /*after_slot, before_slot,*/
"got 'before' is not wanted 'before'");
// reported 'after' varies, depending on group
if(r->after != after_wanted)
rrd2rrdr_log_request_response_metadata(r, options, group_method, aligned, group, resampling_time_requested, resampling_group,
after_wanted, after_requested, before_wanted, before_requested,
points_requested, points_wanted, /*after_slot, before_slot,*/
"got 'after' is not wanted 'after'");
}
#endif
// free all resources used by the grouping method
r->internal.grouping_free(r);
// when all the dimensions are zero, we should return all of them
if(unlikely(options & RRDR_OPTION_NONZERO && !dimensions_nonzero && !(r->result_options & RRDR_RESULT_OPTION_CANCEL))) {
// all the dimensions are zero
// mark them as NONZERO to send them all
for(rd = first_rd, c = 0 ; rd && c < dimensions_count ; rd = rd->next, c++) {
if(unlikely(r->od[c] & RRDR_DIMENSION_HIDDEN)) continue;
r->od[c] |= RRDR_DIMENSION_NONZERO;
}
}
rrdr_query_completed(r->internal.db_points_read, r->internal.result_points_generated);
return r;
}