// SPDX-License-Identifier: GPL-3.0-or-later
#include "daemon/common.h"
#include "database/KolmogorovSmirnovDist.h"
#define MAX_POINTS 10000
int enable_metric_correlations = CONFIG_BOOLEAN_YES;
int metric_correlations_version = 1;
WEIGHTS_METHOD default_metric_correlations_method = WEIGHTS_METHOD_MC_KS2;
typedef struct weights_stats {
NETDATA_DOUBLE max_base_high_ratio;
size_t db_points;
size_t result_points;
size_t db_queries;
size_t db_points_per_tier[RRD_STORAGE_TIERS];
size_t binary_searches;
} WEIGHTS_STATS;
// ----------------------------------------------------------------------------
// parse and render metric correlations methods
static struct {
const char *name;
WEIGHTS_METHOD value;
} weights_methods[] = {
{ "ks2" , WEIGHTS_METHOD_MC_KS2}
, { "volume" , WEIGHTS_METHOD_MC_VOLUME}
, { "anomaly-rate" , WEIGHTS_METHOD_ANOMALY_RATE}
, { NULL , 0 }
};
WEIGHTS_METHOD weights_string_to_method(const char *method) {
for(int i = 0; weights_methods[i].name ;i++)
if(strcmp(method, weights_methods[i].name) == 0)
return weights_methods[i].value;
return default_metric_correlations_method;
}
const char *weights_method_to_string(WEIGHTS_METHOD method) {
for(int i = 0; weights_methods[i].name ;i++)
if(weights_methods[i].value == method)
return weights_methods[i].name;
return "unknown";
}
// ----------------------------------------------------------------------------
// The results per dimension are aggregated into a dictionary
typedef enum {
RESULT_IS_BASE_HIGH_RATIO = (1 << 0),
RESULT_IS_PERCENTAGE_OF_TIME = (1 << 1),
} RESULT_FLAGS;
struct register_result {
RESULT_FLAGS flags;
RRDCONTEXT_ACQUIRED *rca;
RRDINSTANCE_ACQUIRED *ria;
RRDMETRIC_ACQUIRED *rma;
NETDATA_DOUBLE value;
};
static DICTIONARY *register_result_init() {
DICTIONARY *results = dictionary_create(DICT_OPTION_SINGLE_THREADED);
return results;
}
static void register_result_destroy(DICTIONARY *results) {
dictionary_destroy(results);
}
static void register_result(DICTIONARY *results,
RRDCONTEXT_ACQUIRED *rca,
RRDINSTANCE_ACQUIRED *ria,
RRDMETRIC_ACQUIRED *rma,
NETDATA_DOUBLE value,
RESULT_FLAGS flags,
WEIGHTS_STATS *stats,
bool register_zero) {
if(!netdata_double_isnumber(value)) return;
// make it positive
NETDATA_DOUBLE v = fabsndd(value);
// no need to store zero scored values
if(unlikely(fpclassify(v) == FP_ZERO && !register_zero))
return;
// keep track of the max of the baseline / highlight ratio
if(flags & RESULT_IS_BASE_HIGH_RATIO && v > stats->max_base_high_ratio)
stats->max_base_high_ratio = v;
struct register_result t = {
.flags = flags,
.rca = rca,
.ria = ria,
.rma = rma,
.value = v
};
// we can use the pointer address or RMA as a unique key for each metric
char buf[20 + 1];
ssize_t len = snprintfz(buf, 20, "%p", rma);
dictionary_set_advanced(results, buf, len + 1, &t, sizeof(struct register_result), NULL);
}
// ----------------------------------------------------------------------------
// Generation of JSON output for the results
static void results_header_to_json(DICTIONARY *results __maybe_unused, BUFFER *wb,
time_t after, time_t before,
time_t baseline_after, time_t baseline_before,
size_t points, WEIGHTS_METHOD method,
RRDR_GROUPING group, RRDR_OPTIONS options, uint32_t shifts,
size_t examined_dimensions __maybe_unused, usec_t duration,
WEIGHTS_STATS *stats) {
buffer_sprintf(wb, "{\n"
"\t\"after\": %lld,\n"
"\t\"before\": %lld,\n"
"\t\"duration\": %lld,\n"
"\t\"points\": %zu,\n",
(long long)after,
(long long)before,
(long long)(before - after),
points
);
if(method == WEIGHTS_METHOD_MC_KS2 || method == WEIGHTS_METHOD_MC_VOLUME)
buffer_sprintf(wb, ""
"\t\"baseline_after\": %lld,\n"
"\t\"baseline_before\": %lld,\n"
"\t\"baseline_duration\": %lld,\n"
"\t\"baseline_points\": %zu,\n",
(long long)baseline_after,
(long long)baseline_before,
(long long)(baseline_before - baseline_after),
points << shifts
);
buffer_sprintf(wb, ""
"\t\"statistics\": {\n"
"\t\t\"query_time_ms\": %f,\n"
"\t\t\"db_queries\": %zu,\n"
"\t\t\"query_result_points\": %zu,\n"
"\t\t\"binary_searches\": %zu,\n"
"\t\t\"db_points_read\": %zu,\n"
"\t\t\"db_points_per_tier\": [ ",
(double)duration / (double)USEC_PER_MS,
stats->db_queries,
stats->result_points,
stats->binary_searches,
stats->db_points
);
for(size_t tier = 0; tier < storage_tiers ;tier++)
buffer_sprintf(wb, "%s%zu", tier?", ":"", stats->db_points_per_tier[tier]);
buffer_sprintf(wb, " ]\n"
"\t},\n"
"\t\"group\": \"%s\",\n"
"\t\"method\": \"%s\",\n"
"\t\"options\": \"",
web_client_api_request_v1_data_group_to_string(group),
weights_method_to_string(method)
);
web_client_api_request_v1_data_options_to_buffer(wb, options);
}
static size_t registered_results_to_json_charts(DICTIONARY *results, BUFFER *wb,
time_t after, time_t before,
time_t baseline_after, time_t baseline_before,
size_t points, WEIGHTS_METHOD method,
RRDR_GROUPING group, RRDR_OPTIONS options, uint32_t shifts,
size_t examined_dimensions, usec_t duration,
WEIGHTS_STATS *stats) {
results_header_to_json(results, wb, after, before, baseline_after, baseline_before,
points, method, group, options, shifts, examined_dimensions, duration, stats);
buffer_strcat(wb, "\",\n\t\"correlated_charts\": {\n");
size_t charts = 0, chart_dims = 0, total_dimensions = 0;
struct register_result *t;
RRDINSTANCE_ACQUIRED *last_ria = NULL; // never access this - we use it only for comparison
dfe_start_read(results, t) {
if(t->ria != last_ria) {
last_ria = t->ria;
if(charts) buffer_strcat(wb, "\n\t\t\t}\n\t\t},\n");
buffer_strcat(wb, "\t\t\"");
buffer_strcat(wb, rrdinstance_acquired_id(t->ria));
buffer_strcat(wb, "\": {\n");
buffer_strcat(wb, "\t\t\t\"context\": \"");
buffer_strcat(wb, rrdcontext_acquired_id(t->rca));
buffer_strcat(wb, "\",\n\t\t\t\"dimensions\": {\n");
charts++;
chart_dims = 0;
}
if (chart_dims) buffer_sprintf(wb, ",\n");
buffer_sprintf(wb, "\t\t\t\t\"%s\": " NETDATA_DOUBLE_FORMAT, rrdmetric_acquired_name(t->rma), t->value);
chart_dims++;
total_dimensions++;
}
dfe_done(t);
// close dimensions and chart
if (total_dimensions)
buffer_strcat(wb, "\n\t\t\t}\n\t\t}\n");
// close correlated_charts
buffer_sprintf(wb, "\t},\n"
"\t\"correlated_dimensions\": %zu,\n"
"\t\"total_dimensions_count\": %zu\n"
"}\n",
total_dimensions,
examined_dimensions
);
return total_dimensions;
}
static size_t registered_results_to_json_contexts(DICTIONARY *results, BUFFER *wb,
time_t after, time_t before,
time_t baseline_after, time_t baseline_before,
size_t points, WEIGHTS_METHOD method,
RRDR_GROUPING group, RRDR_OPTIONS options, uint32_t shifts,
size_t examined_dimensions, usec_t duration,
WEIGHTS_STATS *stats) {
results_header_to_json(results, wb, after, before, baseline_after, baseline_before,
points, method, group, options, shifts, examined_dimensions, duration, stats);
buffer_strcat(wb, "\",\n\t\"contexts\": {\n");
size_t contexts = 0, charts = 0, total_dimensions = 0, context_dims = 0, chart_dims = 0;
NETDATA_DOUBLE contexts_total_weight = 0.0, charts_total_weight = 0.0;
struct register_result *t;
RRDCONTEXT_ACQUIRED *last_rca = NULL;
RRDINSTANCE_ACQUIRED *last_ria = NULL;
dfe_start_read(results, t) {
if(t->rca != last_rca) {
last_rca = t->rca;
if(contexts)
buffer_sprintf(wb, "\n"
"\t\t\t\t\t},\n"
"\t\t\t\t\t\"weight\":" NETDATA_DOUBLE_FORMAT "\n"
"\t\t\t\t}\n\t\t\t},\n"
"\t\t\t\"weight\":" NETDATA_DOUBLE_FORMAT "\n\t\t},\n"
, charts_total_weight / (double)chart_dims
, contexts_total_weight / (double)context_dims);
buffer_strcat(wb, "\t\t\"");
buffer_strcat(wb, rrdcontext_acquired_id(t->rca));
buffer_strcat(wb, "\": {\n\t\t\t\"charts\":{\n");
contexts++;
charts = 0;
context_dims = 0;
contexts_total_weight = 0.0;
last_ria = NULL;
}
if(t->ria != last_ria) {
last_ria = t->ria;
if(charts)
buffer_sprintf(wb, "\n"
"\t\t\t\t\t},\n"
"\t\t\t\t\t\"weight\":" NETDATA_DOUBLE_FORMAT "\n"
"\t\t\t\t},\n"
, charts_total_weight / (double)chart_dims);
buffer_strcat(wb, "\t\t\t\t\"");
buffer_strcat(wb, rrdinstance_acquired_id(t->ria));
buffer_strcat(wb, "\": {\n");
buffer_strcat(wb, "\t\t\t\t\t\"dimensions\": {\n");
charts++;
chart_dims = 0;
charts_total_weight = 0.0;
}
if (chart_dims) buffer_sprintf(wb, ",\n");
buffer_sprintf(wb, "\t\t\t\t\t\t\"%s\": " NETDATA_DOUBLE_FORMAT, rrdmetric_acquired_name(t->rma), t->value);
charts_total_weight += t->value;
contexts_total_weight += t->value;
chart_dims++;
context_dims++;
total_dimensions++;
}
dfe_done(t);
// close dimensions and chart
if (total_dimensions)
buffer_sprintf(wb, "\n"
"\t\t\t\t\t},\n"
"\t\t\t\t\t\"weight\":" NETDATA_DOUBLE_FORMAT "\n"
"\t\t\t\t}\n"
"\t\t\t},\n"
"\t\t\t\"weight\":" NETDATA_DOUBLE_FORMAT "\n"
"\t\t}\n"
, charts_total_weight / (double)chart_dims
, contexts_total_weight / (double)context_dims);
// close correlated_charts
buffer_sprintf(wb, "\t},\n"
"\t\"weighted_dimensions\": %zu,\n"
"\t\"total_dimensions_count\": %zu\n"
"}\n",
total_dimensions,
examined_dimensions
);
return total_dimensions;
}
// ----------------------------------------------------------------------------
// KS2 algorithm functions
typedef long int DIFFS_NUMBERS;
#define DOUBLE_TO_INT_MULTIPLIER 100000
static inline int binary_search_bigger_than(const DIFFS_NUMBERS arr[], int left, int size, DIFFS_NUMBERS K) {
// binary search to find the index the smallest index
// of the first value in the array that is greater than K
int right = size;
while(left < right) {
int middle = (int)(((unsigned int)(left + right)) >> 1);
if(arr[middle] > K)
right = middle;
else
left = middle + 1;
}
return left;
}
int compare_diffs(const void *left, const void *right) {
DIFFS_NUMBERS lt = *(DIFFS_NUMBERS *)left;
DIFFS_NUMBERS rt = *(DIFFS_NUMBERS *)right;
// https://stackoverflow.com/a/3886497/1114110
return (lt > rt) - (lt < rt);
}
static size_t calculate_pairs_diff(DIFFS_NUMBERS *diffs, NETDATA_DOUBLE *arr, size_t size) {
NETDATA_DOUBLE *last = &arr[size - 1];
size_t added = 0;
while(last > arr) {
NETDATA_DOUBLE second = *last--;
NETDATA_DOUBLE first = *last;
*diffs++ = (DIFFS_NUMBERS)((first - second) * (NETDATA_DOUBLE)DOUBLE_TO_INT_MULTIPLIER);
added++;
}
return added;
}
static double ks_2samp(
DIFFS_NUMBERS baseline_diffs[], int base_size,
DIFFS_NUMBERS highlight_diffs[], int high_size,
uint32_t base_shifts) {
qsort(baseline_diffs, base_size, sizeof(DIFFS_NUMBERS), compare_diffs);
qsort(highlight_diffs, high_size, sizeof(DIFFS_NUMBERS), compare_diffs);
// Now we should be calculating this:
//
// For each number in the diffs arrays, we should find the index of the
// number bigger than them in both arrays and calculate the % of this index
// vs the total array size. Once we have the 2 percentages, we should find
// the min and max across the delta of all of them.
//
// It should look like this:
//
// base_pcent = binary_search_bigger_than(...) / base_size;
// high_pcent = binary_search_bigger_than(...) / high_size;
// delta = base_pcent - high_pcent;
// if(delta < min) min = delta;
// if(delta > max) max = delta;
//
// This would require a lot of multiplications and divisions.
//
// To speed it up, we do the binary search to find the index of each number
// but, then we divide the base index by the power of two number (shifts) it
// is bigger than high index. So the 2 indexes are now comparable.
// We also keep track of the original indexes with min and max, to properly
// calculate their percentages once the loops finish.
// initialize min and max using the first number of baseline_diffs
DIFFS_NUMBERS K = baseline_diffs[0];
int base_idx = binary_search_bigger_than(baseline_diffs, 1, base_size, K);
int high_idx = binary_search_bigger_than(highlight_diffs, 0, high_size, K);
int delta = base_idx - (high_idx << base_shifts);
int min = delta, max = delta;
int base_min_idx = base_idx;
int base_max_idx = base_idx;
int high_min_idx = high_idx;
int high_max_idx = high_idx;
// do the baseline_diffs starting from 1 (we did position 0 above)
for(int i = 1; i < base_size; i++) {
K = baseline_diffs[i];
base_idx = binary_search_bigger_than(baseline_diffs, i + 1, base_size, K); // starting from i, since data1 is sorted
high_idx = binary_search_bigger_than(highlight_diffs, 0, high_size, K);
delta = base_idx - (high_idx << base_shifts);
if(delta < min) {
min = delta;
base_min_idx = base_idx;
high_min_idx = high_idx;
}
else if(delta > max) {
max = delta;
base_max_idx = base_idx;
high_max_idx = high_idx;
}
}
// do the highlight_diffs starting from 0
for(int i = 0; i < high_size; i++) {
K = highlight_diffs[i];
base_idx = binary_search_bigger_than(baseline_diffs, 0, base_size, K);
high_idx = binary_search_bigger_than(highlight_diffs, i + 1, high_size, K); // starting from i, since data2 is sorted
delta = base_idx - (high_idx << base_shifts);
if(delta < min) {
min = delta;
base_min_idx = base_idx;
high_min_idx = high_idx;
}
else if(delta > max) {
max = delta;
base_max_idx = base_idx;
high_max_idx = high_idx;
}
}
// now we have the min, max and their indexes
// properly calculate min and max as dmin and dmax
double dbase_size = (double)base_size;
double dhigh_size = (double)high_size;
double dmin = ((double)base_min_idx / dbase_size) - ((double)high_min_idx / dhigh_size);
double dmax = ((double)base_max_idx / dbase_size) - ((double)high_max_idx / dhigh_size);
dmin = -dmin;
if(islessequal(dmin, 0.0)) dmin = 0.0;
else if(isgreaterequal(dmin, 1.0)) dmin = 1.0;
double d;
if(isgreaterequal(dmin, dmax)) d = dmin;
else d = dmax;
double en = round(dbase_size * dhigh_size / (dbase_size + dhigh_size));
// under these conditions, KSfbar() crashes
if(unlikely(isnan(en) || isinf(en) || en == 0.0 || isnan(d) || isinf(d)))
return NAN;
return KSfbar((int)en, d);
}
static double kstwo(
NETDATA_DOUBLE baseline[], int baseline_points,
NETDATA_DOUBLE highlight[], int highlight_points,
uint32_t base_shifts) {
// -1 in size, since the calculate_pairs_diffs() returns one less point
DIFFS_NUMBERS baseline_diffs[baseline_points - 1];
DIFFS_NUMBERS highlight_diffs[highlight_points - 1];
int base_size = (int)calculate_pairs_diff(baseline_diffs, baseline, baseline_points);
int high_size = (int)calculate_pairs_diff(highlight_diffs, highlight, highlight_points);
if(unlikely(!base_size || !high_size))
return NAN;
if(unlikely(base_size != baseline_points - 1 || high_size != highlight_points - 1)) {
error("Metric correlations: internal error - calculate_pairs_diff() returns the wrong number of entries");
return NAN;
}
return ks_2samp(baseline_diffs, base_size, highlight_diffs, high_size, base_shifts);
}
NETDATA_DOUBLE *rrd2rrdr_ks2(
ONEWAYALLOC *owa, RRDHOST *host,
RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, RRDMETRIC_ACQUIRED *rma,
time_t after, time_t before, size_t points, RRDR_OPTIONS options,
RRDR_GROUPING group_method, const char *group_options, size_t tier,
WEIGHTS_STATS *stats,
size_t *entries
) {
NETDATA_DOUBLE *ret = NULL;
QUERY_TARGET_REQUEST qtr = {
.host = host,
.rca = rca,
.ria = ria,
.rma = rma,
.after = after,
.before = before,
.points = points,
.options = options,
.group_method = group_method,
.group_options = group_options,
.tier = tier,
.query_source = QUERY_SOURCE_API_WEIGHTS,
.priority = STORAGE_PRIORITY_NORMAL,
};
RRDR *r = rrd2rrdr(owa, query_target_create(&qtr));
if(!r)
goto cleanup;
stats->db_queries++;
stats->result_points += r->internal.result_points_generated;
stats->db_points += r->internal.db_points_read;
for(size_t tr = 0; tr < storage_tiers ; tr++)
stats->db_points_per_tier[tr] += r->internal.tier_points_read[tr];
if(r->d != 1) {
error("WEIGHTS: on query '%s' expected 1 dimension in RRDR but got %zu", r->internal.qt->id, r->d);
goto cleanup;
}
if(unlikely(r->od[0] & RRDR_DIMENSION_HIDDEN))
goto cleanup;
if(unlikely(!(r->od[0] & RRDR_DIMENSION_QUERIED)))
goto cleanup;
if(unlikely(!(r->od[0] & RRDR_DIMENSION_NONZERO)))
goto cleanup;
if(rrdr_rows(r) < 2)
goto cleanup;
*entries = rrdr_rows(r);
ret = onewayalloc_mallocz(owa, sizeof(NETDATA_DOUBLE) * rrdr_rows(r));
// copy the points of the dimension to a contiguous array
// there is no need to check for empty values, since empty values are already zero
// https://github.com/netdata/netdata/blob/6e3144683a73a2024d51425b20ecfd569034c858/web/api/queries/average/average.c#L41-L43
memcpy(ret, r->v, rrdr_rows(r) * sizeof(NETDATA_DOUBLE));
cleanup:
rrdr_free(owa, r);
return ret;
}
static void rrdset_metric_correlations_ks2(
RRDHOST *host,
RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, RRDMETRIC_ACQUIRED *rma,
DICTIONARY *results,
time_t baseline_after, time_t baseline_before,
time_t after, time_t before,
size_t points, RRDR_OPTIONS options,
RRDR_GROUPING group_method, const char *group_options, size_t tier,
uint32_t shifts,
WEIGHTS_STATS *stats, bool register_zero
) {
options |= RRDR_OPTION_NATURAL_POINTS;
ONEWAYALLOC *owa = onewayalloc_create(16 * 1024);
size_t high_points = 0;
NETDATA_DOUBLE *highlight = rrd2rrdr_ks2(
owa, host, rca, ria, rma, after, before, points,
options, group_method, group_options, tier, stats, &high_points);
if(!highlight)
goto cleanup;
size_t base_points = 0;
NETDATA_DOUBLE *baseline = rrd2rrdr_ks2(
owa, host, rca, ria, rma, baseline_after, baseline_before, high_points << shifts,
options, group_method, group_options, tier, stats, &base_points);
if(!baseline)
goto cleanup;
stats->binary_searches += 2 * (base_points - 1) + 2 * (high_points - 1);
double prob = kstwo(baseline, (int)base_points, highlight, (int)high_points, shifts);
if(!isnan(prob) && !isinf(prob)) {
// these conditions should never happen, but still let's check
if(unlikely(prob < 0.0)) {
error("Metric correlations: kstwo() returned a negative number: %f", prob);
prob = -prob;
}
if(unlikely(prob > 1.0)) {
error("Metric correlations: kstwo() returned a number above 1.0: %f", prob);
prob = 1.0;
}
// to spread the results evenly, 0.0 needs to be the less correlated and 1.0 the most correlated
// so, we flip the result of kstwo()
register_result(results, rca, ria, rma, 1.0 - prob, RESULT_IS_BASE_HIGH_RATIO, stats, register_zero);
}
cleanup:
onewayalloc_destroy(owa);
}
// ----------------------------------------------------------------------------
// VOLUME algorithm functions
static void merge_query_value_to_stats(QUERY_VALUE *qv, WEIGHTS_STATS *stats) {
stats->db_queries++;
stats->result_points += qv->result_points;
stats->db_points += qv->points_read;
for(size_t tier = 0; tier < storage_tiers ; tier++)
stats->db_points_per_tier[tier] += qv->storage_points_per_tier[tier];
}
static void rrdset_metric_correlations_volume(
RRDHOST *host,
RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, RRDMETRIC_ACQUIRED *rma,
DICTIONARY *results,
time_t baseline_after, time_t baseline_before,
time_t after, time_t before,
RRDR_OPTIONS options, RRDR_GROUPING group_method, const char *group_options,
size_t tier,
WEIGHTS_STATS *stats, bool register_zero) {
options |= RRDR_OPTION_MATCH_IDS | RRDR_OPTION_ABSOLUTE | RRDR_OPTION_NATURAL_POINTS;
QUERY_VALUE baseline_average = rrdmetric2value(host, rca, ria, rma, baseline_after, baseline_before,
options, group_method, group_options, tier, 0,
QUERY_SOURCE_API_WEIGHTS, STORAGE_PRIORITY_NORMAL);
merge_query_value_to_stats(&baseline_average, stats);
if(!netdata_double_isnumber(baseline_average.value)) {
// this means no data for the baseline window, but we may have data for the highlighted one - assume zero
baseline_average.value = 0.0;
}
QUERY_VALUE highlight_average = rrdmetric2value(host, rca, ria, rma, after, before,
options, group_method, group_options, tier, 0,
QUERY_SOURCE_API_WEIGHTS, STORAGE_PRIORITY_NORMAL);
merge_query_value_to_stats(&highlight_average, stats);
if(!netdata_double_isnumber(highlight_average.value))
return;
if(baseline_average.value == highlight_average.value) {
// they are the same - let's move on
return;
}
char highlight_countif_options[50 + 1];
snprintfz(highlight_countif_options, 50, "%s" NETDATA_DOUBLE_FORMAT, highlight_average.value < baseline_average.value ? "<" : ">", baseline_average.value);
QUERY_VALUE highlight_countif = rrdmetric2value(host, rca, ria, rma, after, before,
options, RRDR_GROUPING_COUNTIF, highlight_countif_options, tier, 0,
QUERY_SOURCE_API_WEIGHTS, STORAGE_PRIORITY_NORMAL);
merge_query_value_to_stats(&highlight_countif, stats);
if(!netdata_double_isnumber(highlight_countif.value)) {
info("WEIGHTS: highlighted countif query failed, but highlighted average worked - strange...");
return;
}
// this represents the percentage of time
// the highlighted window was above/below the baseline window
// (above or below depending on their averages)
highlight_countif.value = highlight_countif.value / 100.0; // countif returns 0 - 100.0
RESULT_FLAGS flags;
NETDATA_DOUBLE pcent = NAN;
if(isgreater(baseline_average.value, 0.0) || isless(baseline_average.value, 0.0)) {
flags = RESULT_IS_BASE_HIGH_RATIO;
pcent = (highlight_average.value - baseline_average.value) / baseline_average.value * highlight_countif.value;
}
else {
flags = RESULT_IS_PERCENTAGE_OF_TIME;
pcent = highlight_countif.value;
}
register_result(results, rca, ria, rma, pcent, flags, stats, register_zero);
}
// ----------------------------------------------------------------------------
// ANOMALY RATE algorithm functions
static void rrdset_weights_anomaly_rate(
RRDHOST *host,
RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, RRDMETRIC_ACQUIRED *rma,
DICTIONARY *results,
time_t after, time_t before,
RRDR_OPTIONS options, RRDR_GROUPING group_method, const char *group_options,
size_t tier,
WEIGHTS_STATS *stats, bool register_zero) {
options |= RRDR_OPTION_MATCH_IDS | RRDR_OPTION_ANOMALY_BIT | RRDR_OPTION_NATURAL_POINTS;
QUERY_VALUE qv = rrdmetric2value(host, rca, ria, rma, after, before,
options, group_method, group_options, tier, 0,
QUERY_SOURCE_API_WEIGHTS, STORAGE_PRIORITY_NORMAL);
merge_query_value_to_stats(&qv, stats);
if(netdata_double_isnumber(qv.value))
register_result(results, rca, ria, rma, qv.value, 0, stats, register_zero);
}
// ----------------------------------------------------------------------------
int compare_netdata_doubles(const void *left, const void *right) {
NETDATA_DOUBLE lt = *(NETDATA_DOUBLE *)left;
NETDATA_DOUBLE rt = *(NETDATA_DOUBLE *)right;
// https://stackoverflow.com/a/3886497/1114110
return (lt > rt) - (lt < rt);
}
static inline int binary_search_bigger_than_netdata_double(const NETDATA_DOUBLE arr[], int left, int size, NETDATA_DOUBLE K) {
// binary search to find the index the smallest index
// of the first value in the array that is greater than K
int right = size;
while(left < right) {
int middle = (int)(((unsigned int)(left + right)) >> 1);
if(arr[middle] > K)
right = middle;
else
left = middle + 1;
}
return left;
}
// ----------------------------------------------------------------------------
// spread the results evenly according to their value
static size_t spread_results_evenly(DICTIONARY *results, WEIGHTS_STATS *stats) {
struct register_result *t;
// count the dimensions
size_t dimensions = dictionary_entries(results);
if(!dimensions) return 0;
if(stats->max_base_high_ratio == 0.0)
stats->max_base_high_ratio = 1.0;
// create an array of the right size and copy all the values in it
NETDATA_DOUBLE slots[dimensions];
dimensions = 0;
dfe_start_read(results, t) {
if(t->flags & (RESULT_IS_PERCENTAGE_OF_TIME))
t->value = t->value * stats->max_base_high_ratio;
slots[dimensions++] = t->value;
}
dfe_done(t);
// sort the array with the values of all dimensions
qsort(slots, dimensions, sizeof(NETDATA_DOUBLE), compare_netdata_doubles);
// skip the duplicates in the sorted array
NETDATA_DOUBLE last_value = NAN;
size_t unique_values = 0;
for(size_t i = 0; i < dimensions ;i++) {
if(likely(slots[i] != last_value))
slots[unique_values++] = last_value = slots[i];
}
// this cannot happen, but coverity thinks otherwise...
if(!unique_values)
unique_values = dimensions;
// calculate the weight of each slot, using the number of unique values
NETDATA_DOUBLE slot_weight = 1.0 / (NETDATA_DOUBLE)unique_values;
dfe_start_read(results, t) {
int slot = binary_search_bigger_than_netdata_double(slots, 0, (int)unique_values, t->value);
NETDATA_DOUBLE v = slot * slot_weight;
if(unlikely(v > 1.0)) v = 1.0;
v = 1.0 - v;
t->value = v;
}
dfe_done(t);
return dimensions;
}
// ----------------------------------------------------------------------------
// The main function
int web_api_v1_weights(
RRDHOST *host, BUFFER *wb, WEIGHTS_METHOD method, WEIGHTS_FORMAT format,
RRDR_GROUPING group, const char *group_options,
time_t baseline_after, time_t baseline_before,
time_t after, time_t before,
size_t points, RRDR_OPTIONS options, SIMPLE_PATTERN *contexts, size_t tier, size_t timeout) {
WEIGHTS_STATS stats = {};
DICTIONARY *results = register_result_init();
DICTIONARY *metrics = NULL;
char *error = NULL;
int resp = HTTP_RESP_OK;
// if the user didn't give a timeout
// assume 60 seconds
if(!timeout)
timeout = 60 * MSEC_PER_SEC;
// if the timeout is less than 1 second
// make it at least 1 second
if(timeout < (long)(1 * MSEC_PER_SEC))
timeout = 1 * MSEC_PER_SEC;
usec_t timeout_usec = timeout * USEC_PER_MS;
usec_t started_usec = now_realtime_usec();
if(!rrdr_relative_window_to_absolute(&after, &before))
buffer_no_cacheable(wb);
if (before <= after) {
resp = HTTP_RESP_BAD_REQUEST;
error = "Invalid selected time-range.";
goto cleanup;
}
uint32_t shifts = 0;
if(method == WEIGHTS_METHOD_MC_KS2 || method == WEIGHTS_METHOD_MC_VOLUME) {
if(!points) points = 500;
if(baseline_before <= API_RELATIVE_TIME_MAX)
baseline_before += after;
rrdr_relative_window_to_absolute(&baseline_after, &baseline_before);
if (baseline_before <= baseline_after) {
resp = HTTP_RESP_BAD_REQUEST;
error = "Invalid baseline time-range.";
goto cleanup;
}
// baseline should be a power of two multiple of highlight
long long base_delta = baseline_before - baseline_after;
long long high_delta = before - after;
uint32_t multiplier = (uint32_t)round((double)base_delta / (double)high_delta);
// check if the multiplier is a power of two
// https://stackoverflow.com/a/600306/1114110
if((multiplier & (multiplier - 1)) != 0) {
// it is not power of two
// let's find the closest power of two
// https://stackoverflow.com/a/466242/1114110
multiplier--;
multiplier |= multiplier >> 1;
multiplier |= multiplier >> 2;
multiplier |= multiplier >> 4;
multiplier |= multiplier >> 8;
multiplier |= multiplier >> 16;
multiplier++;
}
// convert the multiplier to the number of shifts
// we need to do, to divide baseline numbers to match
// the highlight ones
while(multiplier > 1) {
shifts++;
multiplier = multiplier >> 1;
}
// if the baseline size will not comply to MAX_POINTS
// lower the window of the baseline
while(shifts && (points << shifts) > MAX_POINTS)
shifts--;
// if the baseline size still does not comply to MAX_POINTS
// lower the resolution of the highlight and the baseline
while((points << shifts) > MAX_POINTS)
points = points >> 1;
if(points < 15) {
resp = HTTP_RESP_BAD_REQUEST;
error = "Too few points available, at least 15 are needed.";
goto cleanup;
}
// adjust the baseline to be multiplier times bigger than the highlight
baseline_after = baseline_before - (high_delta << shifts);
}
size_t examined_dimensions = 0;
bool register_zero = true;
if(options & RRDR_OPTION_NONZERO) {
register_zero = false;
options &= ~RRDR_OPTION_NONZERO;
}
metrics = rrdcontext_all_metrics_to_dict(host, contexts);
struct metric_entry *me;
// for every metric_entry in the dictionary
dfe_start_read(metrics, me) {
usec_t now_usec = now_realtime_usec();
if(now_usec - started_usec > timeout_usec) {
error = "timed out";
resp = HTTP_RESP_GATEWAY_TIMEOUT;
goto cleanup;
}
examined_dimensions++;
switch(method) {
case WEIGHTS_METHOD_ANOMALY_RATE:
options |= RRDR_OPTION_ANOMALY_BIT;
rrdset_weights_anomaly_rate(
host,
me->rca, me->ria, me->rma,
results,
after, before,
options, group, group_options, tier,
&stats, register_zero
);
break;
case WEIGHTS_METHOD_MC_VOLUME:
rrdset_metric_correlations_volume(
host,
me->rca, me->ria, me->rma,
results,
baseline_after, baseline_before,
after, before,
options, group, group_options, tier,
&stats, register_zero
);
break;
default:
case WEIGHTS_METHOD_MC_KS2:
rrdset_metric_correlations_ks2(
host,
me->rca, me->ria, me->rma,
results,
baseline_after, baseline_before,
after, before, points,
options, group, group_options, tier, shifts,
&stats, register_zero
);
break;
}
}
dfe_done(me);
if(!register_zero)
options |= RRDR_OPTION_NONZERO;
if(!(options & RRDR_OPTION_RETURN_RAW))
spread_results_evenly(results, &stats);
usec_t ended_usec = now_realtime_usec();
// generate the json output we need
buffer_flush(wb);
size_t added_dimensions = 0;
switch(format) {
case WEIGHTS_FORMAT_CHARTS:
added_dimensions =
registered_results_to_json_charts(
results, wb,
after, before,
baseline_after, baseline_before,
points, method, group, options, shifts,
examined_dimensions,
ended_usec - started_usec, &stats);
break;
default:
case WEIGHTS_FORMAT_CONTEXTS:
added_dimensions =
registered_results_to_json_contexts(
results, wb,
after, before,
baseline_after, baseline_before,
points, method, group, options, shifts,
examined_dimensions,
ended_usec - started_usec, &stats);
break;
}
if(!added_dimensions) {
error = "no results produced.";
resp = HTTP_RESP_NOT_FOUND;
}
cleanup:
if(metrics) dictionary_destroy(metrics);
if(results) register_result_destroy(results);
if(error) {
buffer_flush(wb);
buffer_sprintf(wb, "{\"error\": \"%s\" }", error);
}
return resp;
}
// ----------------------------------------------------------------------------
// unittest
/*
Unit tests against the output of this:
https://github.com/scipy/scipy/blob/4cf21e753cf937d1c6c2d2a0e372fbc1dbbeea81/scipy/stats/_stats_py.py#L7275-L7449
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import scipy as sp
from scipy import stats
data1 = np.array([ 1111, -2222, 33, 100, 100, 15555, -1, 19999, 888, 755, -1, -730 ])
data2 = np.array([365, -123, 0])
data1 = np.sort(data1)
data2 = np.sort(data2)
n1 = data1.shape[0]
n2 = data2.shape[0]
data_all = np.concatenate([data1, data2])
cdf1 = np.searchsorted(data1, data_all, side='right') / n1
cdf2 = np.searchsorted(data2, data_all, side='right') / n2
print(data_all)
print("\ndata1", data1, cdf1)
print("\ndata2", data2, cdf2)
cddiffs = cdf1 - cdf2
print("\ncddiffs", cddiffs)
minS = np.clip(-np.min(cddiffs), 0, 1)
maxS = np.max(cddiffs)
print("\nmin", minS)
print("max", maxS)
m, n = sorted([float(n1), float(n2)], reverse=True)
en = m * n / (m + n)
d = max(minS, maxS)
prob = stats.distributions.kstwo.sf(d, np.round(en))
print("\nprob", prob)
*/
static int double_expect(double v, const char *str, const char *descr) {
char buf[100 + 1];
snprintfz(buf, 100, "%0.6f", v);
int ret = strcmp(buf, str) ? 1 : 0;
fprintf(stderr, "%s %s, expected %s, got %s\n", ret?"FAILED":"OK", descr, str, buf);
return ret;
}
static int mc_unittest1(void) {
int bs = 3, hs = 3;
DIFFS_NUMBERS base[3] = { 1, 2, 3 };
DIFFS_NUMBERS high[3] = { 3, 4, 6 };
double prob = ks_2samp(base, bs, high, hs, 0);
return double_expect(prob, "0.222222", "3x3");
}
static int mc_unittest2(void) {
int bs = 6, hs = 3;
DIFFS_NUMBERS base[6] = { 1, 2, 3, 10, 10, 15 };
DIFFS_NUMBERS high[3] = { 3, 4, 6 };
double prob = ks_2samp(base, bs, high, hs, 1);
return double_expect(prob, "0.500000", "6x3");
}
static int mc_unittest3(void) {
int bs = 12, hs = 3;
DIFFS_NUMBERS base[12] = { 1, 2, 3, 10, 10, 15, 111, 19999, 8, 55, -1, -73 };
DIFFS_NUMBERS high[3] = { 3, 4, 6 };
double prob = ks_2samp(base, bs, high, hs, 2);
return double_expect(prob, "0.347222", "12x3");
}
static int mc_unittest4(void) {
int bs = 12, hs = 3;
DIFFS_NUMBERS base[12] = { 1111, -2222, 33, 100, 100, 15555, -1, 19999, 888, 755, -1, -730 };
DIFFS_NUMBERS high[3] = { 365, -123, 0 };
double prob = ks_2samp(base, bs, high, hs, 2);
return double_expect(prob, "0.777778", "12x3");
}
int mc_unittest(void) {
int errors = 0;
errors += mc_unittest1();
errors += mc_unittest2();
errors += mc_unittest3();
errors += mc_unittest4();
return errors;
}