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7 Commits

Author SHA1 Message Date
Konstantin Knizhnik
1dfa0e5391 Add missed files 2024-09-20 08:59:06 +03:00
Konstantin Knizhnik
39ad38c980 Make ruff happy: fix test formatting 2024-09-20 07:46:48 +03:00
Konstantin Knizhnik
b71ffd7e60 Add comments and update test 2024-09-20 07:46:48 +03:00
Konstantin Knizhnik
f73128fcaf Fix histogram build 2024-09-20 07:46:48 +03:00
Konstantin Knizhnik
16b0e57d11 Fix test 2024-09-20 07:46:48 +03:00
Konstantin Knizhnik
580946de44 Fix test 2024-09-20 07:46:48 +03:00
Konstantin Knizhnik
61f827364f Add exponential histogram to estiumate optimal cache size 2024-09-20 07:46:47 +03:00
11 changed files with 162 additions and 23 deletions

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@@ -23,7 +23,7 @@ SHLIB_LINK_INTERNAL = $(libpq)
SHLIB_LINK = -lcurl
EXTENSION = neon
DATA = neon--1.0.sql neon--1.0--1.1.sql neon--1.1--1.2.sql neon--1.2--1.3.sql neon--1.3--1.2.sql neon--1.2--1.1.sql neon--1.1--1.0.sql neon--1.3--1.4.sql neon--1.4--1.3.sql
DATA = neon--1.0.sql neon--1.0--1.1.sql neon--1.1--1.2.sql neon--1.2--1.3.sql neon--1.3--1.2.sql neon--1.2--1.1.sql neon--1.1--1.0.sql neon--1.3--1.4.sql neon--1.4--1.3.sql neon--1.4--1.5.sql neon--1.5--1.4.sql
PGFILEDESC = "neon - cloud storage for PostgreSQL"
EXTRA_CLEAN = \

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@@ -1263,7 +1263,7 @@ approximate_working_set_size_seconds(PG_FUNCTION_ARGS)
int32 dc;
time_t duration = PG_ARGISNULL(0) ? (time_t)-1 : PG_GETARG_INT32(0);
LWLockAcquire(lfc_lock, LW_SHARED);
dc = (int32) estimateSHLL(&lfc_ctl->wss_estimation, duration);
dc = (int32) estimateSHLL(&lfc_ctl->wss_estimation, duration, 1.0);
LWLockRelease(lfc_lock);
PG_RETURN_INT32(dc);
}
@@ -1280,7 +1280,7 @@ approximate_working_set_size(PG_FUNCTION_ARGS)
int32 dc;
bool reset = PG_GETARG_BOOL(0);
LWLockAcquire(lfc_lock, reset ? LW_EXCLUSIVE : LW_SHARED);
dc = (int32) estimateSHLL(&lfc_ctl->wss_estimation, (time_t)-1);
dc = (int32) estimateSHLL(&lfc_ctl->wss_estimation, (time_t)-1, 1.0);
if (reset)
memset(lfc_ctl->wss_estimation.regs, 0, sizeof lfc_ctl->wss_estimation.regs);
LWLockRelease(lfc_lock);
@@ -1288,3 +1288,21 @@ approximate_working_set_size(PG_FUNCTION_ARGS)
}
PG_RETURN_NULL();
}
PG_FUNCTION_INFO_V1(approximate_optimal_cache_size);
Datum
approximate_optimal_cache_size(PG_FUNCTION_ARGS)
{
if (lfc_size_limit != 0)
{
int32 dc;
time_t duration = PG_ARGISNULL(0) ? (time_t)-1 : PG_GETARG_INT32(0);
double min_hit_ratio = PG_ARGISNULL(1) ? 1.0 : PG_GETARG_FLOAT8(1);
LWLockAcquire(lfc_lock, LW_SHARED);
dc = (int32) estimateSHLL(&lfc_ctl->wss_estimation, duration, min_hit_ratio);
LWLockRelease(lfc_lock);
PG_RETURN_INT32(dc);
}
PG_RETURN_NULL();
}

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@@ -6,7 +6,7 @@
* Portions Copyright (c) 2014-2023, PostgreSQL Global Development Group
*
* Implements https://hal.science/hal-00465313/document
*
*
* Based on Hideaki Ohno's C++ implementation. This is probably not ideally
* suited to estimating the cardinality of very large sets; in particular, we
* have not attempted to further optimize the implementation as described in
@@ -126,22 +126,78 @@ addSHLL(HyperLogLogState *cState, uint32 hash)
/* Compute the rank of the remaining 32 - "k" (registerWidth) bits */
count = rho(hash << HLL_BIT_WIDTH, HLL_C_BITS);
cState->regs[index][count] = now;
if (cState->regs[index][count].ts)
{
/* update histgoram */
int64_t delta = (now - cState->regs[index][count].ts)/USECS_PER_SEC;
uint32_t new_histogram[HIST_SIZE] = {0};
for (int i = 0; i < HIST_SIZE; i++) {
/* Use middle point of interval */
uint32 interval_log2 = pg_ceil_log2_32((delta + (HIST_MIN_INTERVAL*((1<<i) + ((1<<i)/2))/2)) / HIST_MIN_INTERVAL);
uint32 cell = Min(interval_log2, HIST_SIZE-1);
new_histogram[cell] += cState->regs[index][count].histogram[i];
}
memcpy(cState->regs[index][count].histogram, new_histogram, sizeof new_histogram);
}
cState->regs[index][count].ts = now;
cState->regs[index][count].histogram[0] += 1; // most recent access always goes to first histogram backet
}
static uint32_t
getAccessCount(const HyperLogLogRegister* reg, time_t duration)
{
uint32_t count = 0;
// Simplest solution is to take in account all points fro overlapped interval
// for (size_t i = 0; i < HIST_SIZE && HIST_MIN_INTERVAL*((1 << i)/2) <= duration; i++) {
for (size_t i = 0; i < HIST_SIZE; i++) {
uint32_t high_boundary = HIST_MIN_INTERVAL*(1 << i);
uint32_t low_boundary = HIST_MIN_INTERVAL*((1 << i)/2);
if (high_boundary >= duration) {
// Assume uniform distribution of points within interval and use proportional number of points
Assert(duration >= low_boundary);
count += reg->histogram[i] * (duration - low_boundary) / (high_boundary - low_boundary);
break; // it's last interval within specified time range
} else {
count += reg->histogram[i];
}
}
return count;
}
static uint8
getMaximum(const TimestampTz* reg, TimestampTz since)
getMaximum(const HyperLogLogRegister* reg, TimestampTz since, time_t duration, double min_hit_ratio)
{
uint8 max = 0;
for (size_t i = 0; i < HLL_C_BITS + 1; i++)
size_t i, j;
if (min_hit_ratio == 1.0)
{
if (reg[i] >= since)
for (i = 0; i < HLL_C_BITS + 1; i++)
{
max = i;
if (reg[i].ts >= since)
{
max = i;
}
}
}
else
{
uint32_t total_count = 0;
for (i = 0; i < HLL_C_BITS + 1; i++)
{
total_count += getAccessCount(&reg[i], duration);
}
if (total_count != 0)
{
for (i = 0; i < HLL_C_BITS + 1; i++)
{
// Take in account only bits with access frequncy exceeding maximal miss rate (1 - hit rate)
if (reg[i].ts >= since && 1.0 - (double)getAccessCount(&reg[i], duration) / total_count <= min_hit_ratio)
{
max = i;
}
}
}
}
return max;
}
@@ -150,7 +206,7 @@ getMaximum(const TimestampTz* reg, TimestampTz since)
* Estimates cardinality, based on elements added so far
*/
double
estimateSHLL(HyperLogLogState *cState, time_t duration)
estimateSHLL(HyperLogLogState *cState, time_t duration, double min_hit_ratio)
{
double result;
double sum = 0.0;
@@ -161,7 +217,7 @@ estimateSHLL(HyperLogLogState *cState, time_t duration)
for (i = 0; i < HLL_N_REGISTERS; i++)
{
R[i] = getMaximum(cState->regs[i], since);
R[i] = getMaximum(cState->regs[i], since, duration, min_hit_ratio);
sum += 1.0 / pow(2.0, R[i]);
}

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@@ -53,6 +53,14 @@
#define HLL_C_BITS (32 - HLL_BIT_WIDTH)
#define HLL_N_REGISTERS (1 << HLL_BIT_WIDTH)
/*
* Number of histogram cells. We use exponential histogram with first interval
* equals to one minutes. Autoscaler request LFC statistic with intervals 1,2,...,60 minutes
* so 2^8=64 seems to be enough for our needs.
*/
#define HIST_SIZE 8
#define HIST_MIN_INTERVAL 60 /* seconds */
/*
* HyperLogLog is an approximate technique for computing the number of distinct
* entries in a set. Importantly, it does this by using a fixed amount of
@@ -69,18 +77,21 @@
* modified timestamp >= the query timestamp. This value is the number of bits
* for this register in the normal HLL calculation.
*
* The memory usage is 2^B * (C + 1) * sizeof(TimetampTz), or 184kiB.
* Usage could be halved if we decide to reduce the required time dimension
* precision; as 32 bits in second precision should be enough for statistics.
* However, that is not yet implemented.
* The memory usage is 2^B * (C + 1) * sizeof(HyperLogLogRegister), or 920kiB.
*/
typedef struct
{
TimestampTz ts; /* last access timestamp */
uint32_t histogram[HIST_SIZE]; /* access counter exponential histogram */
} HyperLogLogRegister;
typedef struct HyperLogLogState
{
TimestampTz regs[HLL_N_REGISTERS][HLL_C_BITS + 1];
HyperLogLogRegister regs[HLL_N_REGISTERS][HLL_C_BITS + 1];
} HyperLogLogState;
extern void initSHLL(HyperLogLogState *cState);
extern void addSHLL(HyperLogLogState *cState, uint32 hash);
extern double estimateSHLL(HyperLogLogState *cState, time_t dutration);
extern double estimateSHLL(HyperLogLogState *cState, time_t dutration, double min_hit_ratio);
#endif

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@@ -0,0 +1,10 @@
\echo Use "ALTER EXTENSION neon UPDATE TO '1.5'" to load this file. \quit
-- returns minimal LFC cache size (in 8kb pages) provided specified hit rate
CREATE FUNCTION approximate_optimal_cache_size(duration_sec integer default null, min_hit_ration float8 default null)
RETURNS integer
AS 'MODULE_PATHNAME', 'approximate_optimal_cache_size'
LANGUAGE C PARALLEL SAFE;
GRANT EXECUTE ON FUNCTION approximate_optimal_cache_size(integer,float8) TO pg_monitor;

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@@ -0,0 +1 @@
DROP FUNCTION IF EXISTS approximate_optimal_cache_size(integer,float8) CASCADE;

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@@ -114,3 +114,46 @@ def test_sliding_working_set_approximation(neon_simple_env: NeonEnv):
assert estimation_1k >= 20 and estimation_1k <= 40
assert estimation_10k >= 200 and estimation_10k <= 400
def test_optimal_cache_size_approximation(neon_simple_env: NeonEnv):
env = neon_simple_env
endpoint = env.endpoints.create_start(
branch_name="main",
config_lines=[
"autovacuum = off",
"shared_buffers=1MB",
"neon.max_file_cache_size=256MB",
"neon.file_cache_size_limit=245MB",
],
)
conn = endpoint.connect()
cur = conn.cursor()
cur.execute("create extension neon version '1.5'")
cur.execute(
"create table t_huge(pk integer primary key, count integer default 0, payload text default repeat('?', 128))"
)
cur.execute(
"create table t_small(pk integer primary key, count integer default 0, payload text default repeat('?', 128))"
)
cur.execute(
"insert into t_huge(pk) values (generate_series(1,1000000))"
) # table size is 21277 pages
cur.execute(
"insert into t_small(pk) values (generate_series(1,100000))"
) # table size is 2128 pages
time.sleep(2)
before = time.monotonic()
for _ in range(100):
cur.execute("select sum(count) from t_small")
cur.execute("select sum(count) from t_huge")
after = time.monotonic()
cur.execute(f"select approximate_working_set_size_seconds({int(after - before + 1)})")
ws_estimation = cur.fetchall()[0][0]
log.info(f"Working set size estimaton {ws_estimation}")
cur.execute(f"select approximate_optimal_cache_size({int(after - before + 1)}, 0.99)")
optimal_cache_size = cur.fetchall()[0][0]
log.info(f"Optimal cache size for 99% hit rate {optimal_cache_size}")
assert ws_estimation >= 20000 and ws_estimation <= 30000
assert optimal_cache_size >= 2000 and optimal_cache_size <= 3000