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netdata_netdata/web/api/queries/query.c
Costa Tsaousis 2fc0aaca9a
Query engine with natural and virtual points () 
* new query engine

* use Index

* Revert change that changed in-memory page indexing to start time - update_every + 1

* use internal_error() to cleanup the code

* interpolates values when generating points

Co-authored-by: Stelios Fragkakis <52996999+stelfrag@users.noreply.github.com>
2022-06-29 19:24:08 +03:00

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// SPDX-License-Identifier: GPL-3.0-or-later
#include "query.h"
#include "web/api/formatters/rrd2json.h"
#include "rrdr.h"
#include "database/ram/rrddim_mem.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"
// ----------------------------------------------------------------------------
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);
} 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
},
{.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
},
{.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
},
{.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
},
{.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
},
{.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
},
{.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
},
{.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
},
// 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
},
{.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
},
{.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
},
/*
{.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
},
*/
/*
{.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
},
*/
// 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
},
{.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
},
{.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
},
// 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
},
{.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
},
// 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
}
};
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;
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;
}
}
// ----------------------------------------------------------------------------
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 (should_lock)
rrdset_check_rdlock(r->st);
if(unlikely(!dims || !*dims || (dims[0] == '*' && dims[1] == '\0'))) return;
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, d->id))
|| (match_names && simple_pattern_matches(pattern, d->name))
) {
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'",
r->st->name);
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, r->st->name);
// 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;
}
// ----------------------------------------------------------------------------
// fill RRDR for a single dimension
static inline NETDATA_DOUBLE interpolate_value(NETDATA_DOUBLE this_value, NETDATA_DOUBLE last_value, time_t last_value_end_t, time_t this_value_start_t, time_t now, time_t this_value_end_t) {
if(unlikely(
this_value_start_t + 1 == this_value_end_t ||
!netdata_double_isnumber(this_value) ||
!netdata_double_isnumber(last_value) ||
last_value_end_t != this_value_start_t))
return this_value;
return last_value + (this_value - last_value) * ( 1.0 - (NETDATA_DOUBLE)(this_value_end_t - now) / (NETDATA_DOUBLE)(this_value_end_t - this_value_start_t) );
}
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
, RRDR_OPTIONS options
){
time_t now = after_wanted,
query_granularity = r->update_every / r->group,
max_date = 0,
min_date = 0;
bool interpolate = query_granularity < rd->update_every;
long group_points_wanted = r->group,
points_added = 0, group_points_added = 0, group_points_non_zero = 0,
rrdr_line = -1;
size_t group_anomaly_rate = 0;
RRDR_VALUE_FLAGS group_value_flags = RRDR_VALUE_NOTHING;
struct rrddim_query_handle handle;
NETDATA_DOUBLE min = r->min, max = r->max;
size_t db_points_read = 0;
// cache the function pointers we need in the loop
NETDATA_DOUBLE (*next_metric)(struct rrddim_query_handle *handle, time_t *current_time, time_t *end_time, SN_FLAGS *flags) = rd->state->query_ops.next_metric;
void (*grouping_add)(struct rrdresult *r, NETDATA_DOUBLE value) = r->internal.grouping_add;
NETDATA_DOUBLE (*grouping_flush)(struct rrdresult *r, RRDR_VALUE_FLAGS *rrdr_value_options_ptr) = r->internal.grouping_flush;
NETDATA_DOUBLE last2_point_value;
//SN_FLAGS last2_point_flags;
//size_t last2_point_anomaly;
//time_t last2_point_start_time;
time_t last2_point_end_time;
NETDATA_DOUBLE last1_point_value = NAN;
SN_FLAGS last1_point_flags = SN_EMPTY_SLOT;
size_t last1_point_anomaly = 0;
time_t last1_point_start_time = 0;
time_t last1_point_end_time = 0;
NETDATA_DOUBLE new_point_value = NAN;
SN_FLAGS new_point_flags = SN_EMPTY_SLOT;
size_t new_point_anomaly = 0;
time_t new_point_start_time = 0;
time_t new_point_end_time = 0;
for(rd->state->query_ops.init(rd, &handle, now, before_wanted) ; points_added < points_wanted ; now += query_granularity) {
if(unlikely(now > before_wanted))
break;
last2_point_value = last1_point_value;
//last2_point_flags = last1_point_flags;
//last2_point_anomaly = last1_point_anomaly;
//last2_point_start_time = last1_point_start_time;
last2_point_end_time = last1_point_end_time;
last1_point_value = new_point_value;
last1_point_flags = new_point_flags;
last1_point_anomaly = new_point_anomaly;
last1_point_start_time = new_point_start_time;
last1_point_end_time = new_point_end_time;
if(likely(!rd->state->query_ops.is_finished(&handle))) {
// fetch the new point
new_point_value = next_metric(&handle, &new_point_start_time, &new_point_end_time, &new_point_flags);
db_points_read++;
// dbengine does not take into account the starting time of points
// and depending on the data collection frequency it may return
// a point that is just before the wanted one.
// So, here we fetch the next one.
if(unlikely(new_point_end_time < now)) {
internal_error(true, "QUERY: next_metric(%s, %s) returned point %zu from %ld to %ld, before now (now = %ld, after_wanted = %ld, before_wanted = %ld, dt = %ld). Fetching the next one.",
rd->rrdset->name, rd->name, db_points_read, new_point_start_time, new_point_end_time, now, after_wanted, before_wanted, query_granularity);
new_point_value = next_metric(&handle, &new_point_start_time, &new_point_end_time, &new_point_flags);
db_points_read++;
}
if(likely(netdata_double_isnumber(new_point_value))) {
new_point_anomaly = (new_point_flags & SN_ANOMALY_BIT) ? 0 : 100;
if(unlikely(options & RRDR_OPTION_ANOMALY_BIT))
new_point_value = (NETDATA_DOUBLE)new_point_anomaly;
}
else {
new_point_flags = SN_EMPTY_SLOT;
new_point_value = NAN;
new_point_anomaly = 0;
}
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",
rd->rrdset->name, rd->name, db_points_read, new_point_start_time, new_point_end_time);
new_point_start_time = new_point_end_time - rd->update_every;
}
if(unlikely(new_point_start_time < last1_point_start_time && new_point_end_time < last1_point_end_time)) {
internal_error(true, "QUERY: next_metric(%s, %s) returned point %zu start time %ld, end time %ld, before the last point start time %ld, end time %ld",
rd->rrdset->name, rd->name, db_points_read, new_point_start_time, new_point_end_time,
last1_point_start_time,
last1_point_end_time);
new_point_value = last1_point_value;
new_point_flags = last1_point_flags;
new_point_start_time = last1_point_start_time;
new_point_end_time = last1_point_end_time;
}
if(unlikely(new_point_end_time < last1_point_end_time)) {
internal_error(true, "QUERY: next_metric(%s, %s) returned point %zu end time %ld, before the last point end time %ld",
rd->rrdset->name, rd->name, db_points_read, new_point_end_time,
last1_point_end_time);
new_point_value = last1_point_value;
new_point_flags = last1_point_flags;
new_point_start_time = last1_point_start_time;
new_point_end_time = last1_point_end_time;
}
if(unlikely(new_point_end_time < now)) {
internal_error(true, "QUERY: next_metric(%s, %s) returned point %zu from %ld to %ld, before now (now = %ld, after_wanted = %ld, before_wanted = %ld, dt = %ld)",
rd->rrdset->name, rd->name, db_points_read, new_point_start_time, new_point_end_time,
now, after_wanted, before_wanted, query_granularity);
new_point_end_time = now;
}
}
else {
new_point_value = NAN;
new_point_flags = SN_EMPTY_SLOT;
new_point_start_time = last1_point_end_time;
new_point_end_time = now;
}
// the inner loop
// we have 3 points in memory: last, new, next
// we select the one to use based on their timestamps
size_t iterations = 0;
for ( ; now <= new_point_end_time && points_added < points_wanted; now += query_granularity, iterations++) {
NETDATA_DOUBLE current_point_value;
SN_FLAGS current_point_flags;
size_t current_point_anomaly;
//time_t current_point_start_time;
//time_t current_point_end_time;
if(likely(now > new_point_start_time)) {
// it is time for our NEW point to be used
current_point_value = interpolate ? interpolate_value(new_point_value, last1_point_value, last1_point_end_time, new_point_start_time, now, new_point_end_time) : new_point_value;
current_point_flags = new_point_flags;
current_point_anomaly = new_point_anomaly;
//current_point_start_time = new_point_start_time;
//current_point_end_time = new_point_end_time;
}
else if(likely(now <= last1_point_end_time)) {
// our LAST point is still valid
current_point_value = interpolate ? interpolate_value(last1_point_value, last2_point_value, last2_point_end_time, last1_point_start_time, now, last1_point_end_time) : last1_point_value;
current_point_flags = last1_point_flags;
current_point_anomaly = last1_point_anomaly;
//current_point_start_time = last_point_start_time;
//current_point_end_time = last_point_end_time;
}
else {
// a GAP, we don't have a value this time
current_point_value = NAN;
current_point_flags = SN_EMPTY_SLOT;
current_point_anomaly = 0;
//current_point_start_time = now - dt;
//current_point_end_time = now;
}
if(likely(netdata_double_isnumber(current_point_value))) {
if(likely(current_point_value != 0.0))
group_points_non_zero++;
if(unlikely(current_point_flags & SN_EXISTS_RESET))
group_value_flags |= RRDR_VALUE_RESET;
grouping_add(r, current_point_value);
}
// add this value for grouping
group_points_added++;
group_anomaly_rate += current_point_anomaly;
if(unlikely(group_points_added == group_points_wanted)) {
rrdr_line = rrdr_line_init(r, now, rrdr_line);
size_t rrdr_o_v_index = rrdr_line * r->d + dim_id_in_rrdr;
if(unlikely(!min_date)) min_date = now;
max_date = now;
// 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(group_points_non_zero))
r->od[dim_id_in_rrdr] |= RRDR_DIMENSION_NONZERO;
// store the specific point options
*rrdr_value_options_ptr = group_value_flags;
// store the group value
NETDATA_DOUBLE group_value = 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
group_anomaly_rate = (group_anomaly_rate << 1) / group_points_added;
r->ar[rrdr_o_v_index] = (uint8_t)group_anomaly_rate;
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++;
group_points_added = 0;
group_value_flags = RRDR_VALUE_NOTHING;
group_points_non_zero = 0;
group_anomaly_rate = 0;
}
}
// the loop above increased "now" by dt,
// but the main loop will increase it,
// so, let's undo the last iteration of this loop
if(iterations)
now -= query_granularity;
}
rd->state->query_ops.finalize(&handle);
r->internal.db_points_read += db_points_read;
r->internal.result_points_generated += points_added;
r->min = min;
r->max = max;
r->before = max_date;
r->after = min_date - (r->group - 1) * query_granularity;
rrdr_done(r, rrdr_line);
internal_error(points_wanted != points_added,
"QUERY: query on %s/%s requested %zu points, but RRDR added %zu (%zu db points read).",
r->st->name, rd->name, (size_t)points_wanted, (size_t)points_added, db_points_read);
}
// ----------------------------------------------------------------------------
// fill RRDR for the whole chart
#ifdef NETDATA_INTERNAL_CHECKS
static void rrd2rrdr_log_request_response_metadata(RRDR *r
, 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
) {
netdata_rwlock_rdlock(&r->st->rrdset_rwlock);
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: %zu, db: %zu), "
"before (got: %zu, want: %zu, req: %zu, db: %zu), "
"duration (got: %zu, want: %zu, req: %zu, db: %zu), "
//"slot (after: %zu, before: %zu, delta: %zu), "
"points (got: %ld, want: %ld, req: %ld, db: %ld), "
"%s"
, r->st->name
, 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
, (size_t)after_requested
, (size_t)rrdset_first_entry_t_nolock(r->st)
// before
, (size_t)r->before
, (size_t)before_wanted
, (size_t)before_requested
, (size_t)rrdset_last_entry_t_nolock(r->st)
// duration
, (size_t)(r->before - r->after + r->st->update_every)
, (size_t)(before_wanted - after_wanted + r->st->update_every)
, (size_t)(before_requested - after_requested)
, (size_t)((rrdset_last_entry_t_nolock(r->st) - rrdset_first_entry_t_nolock(r->st)) + 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
);
netdata_rwlock_unlock(&r->st->rrdset_rwlock);
}
#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, int update_every, long points) {
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 = -(points * update_every);
after_requested = before_requested + after_requested;
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, 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, 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; \
}
#else
#define query_debug_log_init() debug_dummy()
#define query_debug_log(args...) debug_dummy()
#define query_debug_log_fin() 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
) {
// 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;
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");
}
// 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;
query_debug_log(":query_granularity %ld", query_granularity);
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;
rrdset_rdlock(st);
time_t first_entry_t = rrdset_first_entry_t_nolock(st);
time_t last_entry_t = rrdset_last_entry_t_nolock(st);
rrdset_unlock(st);
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;
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, (int)query_granularity, points_wanted);
query_debug_log(":relative2absolute after %lld, before %lld", after_wanted, before_wanted);
// 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;
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;
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);
// 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)) {
// alignment has been requested, so align the end timestamp
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);
// 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",
st->id, (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",
st->id, (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;
// -------------------------------------------------------------------------
// 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)
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, options);
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",
st->name, (size_t)max_after, rd->name, (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",
st->name, (size_t)min_before, rd->name, (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",
st->name, (size_t)max_rows, rd->name, (size_t)r->rows);
r->rows = (r->rows > max_rows) ? r->rows : max_rows;
}
}
dimensions_used++;
if (timeout && (dt_usec(&query_start_time, &query_current_time) / 1000.0) > timeout) {
log_access("QUERY CANCELED RUNTIME EXCEEDED %0.2f ms (LIMIT %d ms)",
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, group_method, aligned, group, resampling_time_requested, resampling_group, after_wanted,
after_wanted, before_wanted,
before_wanted,
points_wanted, points_wanted, /*after_slot, before_slot,*/ r->internal.log);
if(r->rows != points_wanted)
rrd2rrdr_log_request_response_metadata(r, group_method, aligned, group, resampling_time_requested, resampling_group, after_wanted,
after_wanted, before_wanted,
before_wanted,
points_wanted, 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, group_method, aligned, group, resampling_time_requested, resampling_group, after_wanted,
after_wanted, before_wanted,
before_wanted,
points_wanted, 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, 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, group_method, aligned, group, resampling_time_requested, resampling_group, after_wanted,
after_wanted, before_wanted,
before_wanted,
points_wanted, points_wanted, /*after_slot, before_slot,*/ "chart is not aligned to requested 'before'");
if(r->before != before_wanted)
rrd2rrdr_log_request_response_metadata(r, group_method, aligned, group, resampling_time_requested, resampling_group, after_wanted,
after_wanted, before_wanted,
before_wanted,
points_wanted, 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, group_method, aligned, group, resampling_time_requested, resampling_group, after_wanted,
after_wanted, before_wanted,
before_wanted,
points_wanted, 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;
}
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