// 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 "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);
// 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, calculated_number 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).
calculated_number (*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
},
// 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;
}
// ----------------------------------------------------------------------------
static void rrdr_disable_not_selected_dimensions(RRDR *r, RRDR_OPTIONS options, const char *dims) {
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 = 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 = 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 *rrdr_line_options(RRDR *r, long rrdr_line) {
return &r->o[ rrdr_line * r->d ];
}
static inline calculated_number *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++;
#ifdef NETDATA_INTERNAL_CHECKS
if(unlikely(rrdr_line >= r->n))
error("INTERNAL ERROR: requested to step above RRDR size for chart '%s'", r->st->name);
if(unlikely(r->t[rrdr_line] != 0 && r->t[rrdr_line] != t))
error("INTERNAL ERROR: 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);
#endif
// 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 void do_dimension(
RRDR *r
, long points_wanted
, RRDDIM *rd
, long dim_id_in_rrdr
, time_t after_wanted
, time_t before_wanted
){
RRDSET *st = r->st;
time_t
now = after_wanted,
dt = st->update_every,
max_date = 0,
min_date = 0;
long
group_size = r->group,
points_added = 0,
values_in_group = 0,
values_in_group_non_zero = 0,
rrdr_line = -1;
RRDR_VALUE_FLAGS
group_value_flags = RRDR_VALUE_NOTHING;
struct rrddim_query_handle handle;
uint8_t initialized_query;
calculated_number min = r->min, max = r->max;
size_t db_points_read = 0;
for(initialized_query = 0 ; points_added < points_wanted ; now += dt) {
// make sure we return data in the proper time range
if(unlikely(now > before_wanted)) {
#ifdef NETDATA_INTERNAL_CHECKS
r->internal.log = "stopped, because attempted to access the db after 'wanted before'";
#endif
break;
}
if(unlikely(now < after_wanted)) {
#ifdef NETDATA_INTERNAL_CHECKS
r->internal.log = "skipped, because attempted to access the db before 'wanted after'";
#endif
continue;
}
if (unlikely(!initialized_query)) {
rd->state->query_ops.init(rd, &handle, now, before_wanted);
initialized_query = 1;
}
// read the value from the database
//storage_number n = rd->values[slot];
#ifdef NETDATA_INTERNAL_CHECKS
if (rd->rrd_memory_mode == RRD_MEMORY_MODE_DBENGINE) {
#ifdef ENABLE_DBENGINE
if (now != handle.rrdeng.now)
error("INTERNAL CHECK: Unaligned query for %s, database time: %ld, expected time: %ld", rd->id, (long)handle.rrdeng.now, (long)now);
#endif
} else if (rrdset_time2slot(st, now) != (long unsigned)handle.slotted.slot) {
error("INTERNAL CHECK: Unaligned query for %s, database slot: %lu, expected slot: %lu", rd->id, (long unsigned)handle.slotted.slot, rrdset_time2slot(st, now));
}
#endif
storage_number n = rd->state->query_ops.next_metric(&handle);
calculated_number value = NAN;
if(likely(does_storage_number_exist(n))) {
value = unpack_storage_number(n);
if(likely(value != 0.0))
values_in_group_non_zero++;
if(unlikely(did_storage_number_reset(n)))
group_value_flags |= RRDR_VALUE_RESET;
}
// add this value for grouping
r->internal.grouping_add(r, value);
values_in_group++;
db_points_read++;
if(unlikely(values_in_group == group_size)) {
rrdr_line = rrdr_line_init(r, now, rrdr_line);
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_line * r->d + dim_id_in_rrdr];
// update the dimension options
if(likely(values_in_group_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 value
calculated_number value = r->internal.grouping_flush(r, rrdr_value_options_ptr);
r->v[rrdr_line * r->d + dim_id_in_rrdr] = value;
if(likely(points_added || dim_id_in_rrdr)) {
// find the min/max across all dimensions
if(unlikely(value < min)) min = value;
if(unlikely(value > max)) max = value;
}
else {
// runs only when dim_id_in_rrdr == 0 && points_added == 0
// so, on the first point added for the query.
min = max = value;
}
points_added++;
values_in_group = 0;
group_value_flags = RRDR_VALUE_NOTHING;
values_in_group_non_zero = 0;
}
}
if (likely(initialized_query))
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) * r->st->update_every;
rrdr_done(r, rrdr_line);
#ifdef NETDATA_INTERNAL_CHECKS
if(unlikely(r->rows != points_added))
error("INTERNAL ERROR: %s.%s added %zu rows, but RRDR says I added %zu.", r->st->name, rd->name, (size_t)points_added, (size_t)r->rows);
#endif
}
// ----------------------------------------------------------------------------
// fill RRDR for the whole chart
#ifdef NETDATA_INTERNAL_CHECKS
static void rrd2rrdr_log_request_response_metdata(RRDR *r
, RRDR_GROUPING group_method
, int 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
) {
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(r->st)
// before
, (size_t)r->before
, (size_t)before_wanted
, (size_t)before_requested
, (size_t)rrdset_last_entry_t(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(r->st) - rrdset_first_entry_t(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
);
}
#endif // NETDATA_INTERNAL_CHECKS
RRDR *rrd2rrdr(
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
) {
int aligned = !(options & RRDR_OPTION_NOT_ALIGNED);
int absolute_period_requested = -1;
time_t first_entry_t = rrdset_first_entry_t(st);
time_t last_entry_t = rrdset_last_entry_t(st);
if(before_requested == 0 && after_requested == 0) {
// dump the all the data
before_requested = last_entry_t;
after_requested = first_entry_t;
absolute_period_requested = 0;
}
// allow relative for before (smaller than API_RELATIVE_TIME_MAX)
if(((before_requested < 0)?-before_requested:before_requested) <= API_RELATIVE_TIME_MAX) {
if(abs(before_requested) % st->update_every) {
// make sure it is multiple of st->update_every
if(before_requested < 0) before_requested = before_requested - st->update_every - before_requested % st->update_every;
else before_requested = before_requested + st->update_every - before_requested % st->update_every;
}
if(before_requested > 0) before_requested = first_entry_t + before_requested;
else before_requested = last_entry_t + before_requested;
absolute_period_requested = 0;
}
// allow relative for after (smaller than API_RELATIVE_TIME_MAX)
if(((after_requested < 0)?-after_requested:after_requested) <= API_RELATIVE_TIME_MAX) {
if(after_requested == 0) after_requested = -st->update_every;
if(abs(after_requested) % st->update_every) {
// make sure it is multiple of st->update_every
if(after_requested < 0) after_requested = after_requested - st->update_every - after_requested % st->update_every;
else after_requested = after_requested + st->update_every - after_requested % st->update_every;
}
after_requested = before_requested + after_requested;
absolute_period_requested = 0;
}
if(absolute_period_requested == -1)
absolute_period_requested = 1;
// make sure they are within our timeframe
if(before_requested > last_entry_t) before_requested = last_entry_t;
if(before_requested < first_entry_t) before_requested = first_entry_t;
if(after_requested > last_entry_t) after_requested = last_entry_t;
if(after_requested < first_entry_t) after_requested = first_entry_t;
// check if they are reversed
if(after_requested > before_requested) {
time_t tmp = before_requested;
before_requested = after_requested;
after_requested = tmp;
}
// the duration of the chart
time_t duration = before_requested - after_requested;
long available_points = duration / st->update_every;
if(duration <= 0 || available_points <= 0)
return rrdr_create(st, 1);
// check the number of wanted points in the result
if(unlikely(points_requested < 0)) points_requested = -points_requested;
if(unlikely(points_requested > available_points)) points_requested = available_points;
if(unlikely(points_requested == 0)) points_requested = available_points;
// calculate the desired grouping of source data points
long group = available_points / points_requested;
if(unlikely(group <= 0)) group = 1;
if(unlikely(available_points % points_requested > points_requested / 2)) group++; // rounding to the closest integer
// resampling_time_requested enforces a certain grouping multiple
calculated_number resampling_divisor = 1.0;
long resampling_group = 1;
if(unlikely(resampling_time_requested > st->update_every)) {
if (unlikely(resampling_time_requested > duration)) {
// group_time is above the available duration
#ifdef NETDATA_INTERNAL_CHECKS
info("INTERNAL CHECK: %s: requested gtime %ld secs, is greater than the desired duration %ld secs", st->id, resampling_time_requested, duration);
#endif
after_requested = before_requested - resampling_time_requested;
duration = before_requested - after_requested;
available_points = duration / st->update_every;
group = available_points / points_requested;
}
// 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(duration % resampling_time_requested) {
time_t delta = duration % resampling_time_requested;
if(delta > resampling_time_requested / 10) {
after_requested -= resampling_time_requested - delta;
duration = before_requested - after_requested;
available_points = duration / st->update_every;
group = available_points / points_requested;
}
}
// the points we should group to satisfy gtime
resampling_group = resampling_time_requested / st->update_every;
if(unlikely(resampling_time_requested % st->update_every)) {
#ifdef NETDATA_INTERNAL_CHECKS
info("INTERNAL CHECK: %s: requested gtime %ld secs, is not a multiple of the chart's data collection frequency %d secs", st->id, resampling_time_requested, st->update_every);
#endif
resampling_group++;
}
// adapt group according to resampling_group
if(unlikely(group < resampling_group)) group = resampling_group; // do not allow grouping below the desired one
if(unlikely(group % resampling_group)) group += resampling_group - (group % resampling_group); // make sure group is multiple of resampling_group
//resampling_divisor = group / resampling_group;
resampling_divisor = (calculated_number)(group * st->update_every) / (calculated_number)resampling_time_requested;
}
// now that we have group,
// align the requested timeframe to fit it.
if(aligned) {
// alignement has been requested, so align the values
before_requested -= (before_requested % group);
after_requested -= (after_requested % group);
}
// we align the request on requested_before
time_t before_wanted = before_requested;
if(likely(before_wanted > last_entry_t)) {
#ifdef NETDATA_INTERNAL_CHECKS
error("INTERNAL ERROR: rrd2rrdr() on %s, before_wanted is after db max", st->name);
#endif
before_wanted = last_entry_t - (last_entry_t % ( ((aligned)?group:1) * st->update_every ));
}
//size_t before_slot = rrdset_time2slot(st, before_wanted);
// we need to estimate the number of points, for having
// an integer number of values per point
long points_wanted = (before_wanted - after_requested) / (st->update_every * group);
time_t after_wanted = before_wanted - (points_wanted * group * st->update_every) + st->update_every;
if(unlikely(after_wanted < first_entry_t)) {
// hm... we go to the past, calculate again points_wanted using all the db from before_wanted to the beginning
points_wanted = (before_wanted - first_entry_t) / group;
// recalculate after wanted with the new number of points
after_wanted = before_wanted - (points_wanted * group * st->update_every) + st->update_every;
if(unlikely(after_wanted < first_entry_t)) {
#ifdef NETDATA_INTERNAL_CHECKS
error("INTERNAL ERROR: rrd2rrdr() on %s, after_wanted is before db min", st->name);
#endif
after_wanted = first_entry_t - (first_entry_t % ( ((aligned)?group:1) * st->update_every )) + ( ((aligned)?group:1) * st->update_every );
}
}
//size_t after_slot = rrdset_time2slot(st, after_wanted);
// check if they are reversed
if(unlikely(after_wanted > before_wanted)) {
#ifdef NETDATA_INTERNAL_CHECKS
error("INTERNAL ERROR: rrd2rrdr() on %s, reversed wanted after/before", st->name);
#endif
time_t tmp = before_wanted;
before_wanted = after_wanted;
after_wanted = tmp;
}
// recalculate points_wanted using the final time-frame
points_wanted = (before_wanted - after_wanted) / st->update_every / group + 1;
if(unlikely(points_wanted < 0)) {
#ifdef NETDATA_INTERNAL_CHECKS
error("INTERNAL ERROR: rrd2rrdr() on %s, points_wanted is %ld", st->name, points_wanted);
#endif
points_wanted = 0;
}
#ifdef NETDATA_INTERNAL_CHECKS
duration = before_wanted - after_wanted;
if(after_wanted < first_entry_t)
error("INTERNAL CHECK: after_wanted %u is too small, minimum %u", (uint32_t)after_wanted, (uint32_t)first_entry_t);
if(after_wanted > last_entry_t)
error("INTERNAL CHECK: after_wanted %u is too big, maximum %u", (uint32_t)after_wanted, (uint32_t)last_entry_t);
if(before_wanted < first_entry_t)
error("INTERNAL CHECK: before_wanted %u is too small, minimum %u", (uint32_t)before_wanted, (uint32_t)first_entry_t);
if(before_wanted > last_entry_t)
error("INTERNAL CHECK: before_wanted %u is too big, maximum %u", (uint32_t)before_wanted, (uint32_t)last_entry_t);
/*
if(before_slot >= (size_t)st->entries)
error("INTERNAL CHECK: before_slot is invalid %zu, expected 0 to %ld", before_slot, st->entries - 1);
if(after_slot >= (size_t)st->entries)
error("INTERNAL CHECK: after_slot is invalid %zu, expected 0 to %ld", after_slot, st->entries - 1);
*/
if(points_wanted > (before_wanted - after_wanted) / group / st->update_every + 1)
error("INTERNAL CHECK: points_wanted %ld is more than points %ld", points_wanted, (before_wanted - after_wanted) / group / st->update_every + 1);
if(group < resampling_group)
error("INTERNAL CHECK: group %ld is less than the desired group points %ld", group, resampling_group);
if(group > resampling_group && group % resampling_group)
error("INTERNAL CHECK: group %ld is not a multiple of the desired group points %ld", group, resampling_group);
#endif
// -------------------------------------------------------------------------
// initialize our result set
// this also locks the chart for us
RRDR *r = rrdr_create(st, points_wanted);
if(unlikely(!r)) {
#ifdef NETDATA_INTERNAL_CHECKS
error("INTERNAL CHECK: 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);
#endif
return NULL;
}
if(unlikely(!r->d || !points_wanted)) {
#ifdef NETDATA_INTERNAL_CHECKS
error("INTERNAL CHECK: 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);
#endif
return r;
}
if(unlikely(absolute_period_requested == 1))
r->result_options |= RRDR_RESULT_OPTION_ABSOLUTE;
else
r->result_options |= RRDR_RESULT_OPTION_RELATIVE;
// find how many dimensions we have
long dimensions_count = r->d;
// -------------------------------------------------------------------------
// initialize RRDR
r->group = group;
r->update_every = (int)group * st->update_every;
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
{
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;
#ifdef NETDATA_INTERNAL_CHECKS
error("INTERNAL ERROR: grouping method %u not found for chart '%s'. Using 'average'", (unsigned int)group_method, r->st->name);
#endif
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;
}
}
// allocate any memory required by the grouping method
r->internal.grouping_data = r->internal.grouping_create(r);
// -------------------------------------------------------------------------
// disable the not-wanted dimensions
rrdset_check_rdlock(st);
if(dimensions)
rrdr_disable_not_selected_dimensions(r, options, dimensions);
// -------------------------------------------------------------------------
// do the work for each dimension
time_t max_after = 0, min_before = 0;
long max_rows = 0;
RRDDIM *rd;
long c, dimensions_used = 0, dimensions_nonzero = 0;
for(rd = st->dimensions, 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);
do_dimension(
r
, points_wanted
, rd
, c
, after_wanted
, before_wanted
);
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) {
#ifdef NETDATA_INTERNAL_CHECKS
error("INTERNAL ERROR: '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);
#endif
r->after = (r->after > max_after) ? r->after : max_after;
}
if(r->before != min_before) {
#ifdef NETDATA_INTERNAL_CHECKS
error("INTERNAL ERROR: '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);
#endif
r->before = (r->before < min_before) ? r->before : min_before;
}
if(r->rows != max_rows) {
#ifdef NETDATA_INTERNAL_CHECKS
error("INTERNAL ERROR: '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);
#endif
r->rows = (r->rows > max_rows) ? r->rows : max_rows;
}
}
dimensions_used++;
}
#ifdef NETDATA_INTERNAL_CHECKS
if(r->internal.log)
rrd2rrdr_log_request_response_metdata(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,*/ r->internal.log);
if(r->rows != points_wanted)
rrd2rrdr_log_request_response_metdata(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,*/ "got 'points' is not wanted 'points'");
if(aligned && (r->before % group) != 0)
rrd2rrdr_log_request_response_metdata(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,*/ "'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_metdata(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_requested)
rrd2rrdr_log_request_response_metdata(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,*/ "chart is not aligned to requested 'before'");
if(r->before != before_wanted)
rrd2rrdr_log_request_response_metdata(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,*/ "got 'before' is not wanted 'before'");
// reported 'after' varies, depending on group
if(r->after != after_wanted)
rrd2rrdr_log_request_response_metdata(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,*/ "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)) {
// all the dimensions are zero
// mark them as NONZERO to send them all
for(rd = st->dimensions, 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;
}