Pysim more algorithms (#5644)

Summary:
This PR adds four more eviction policies.
- OPT [1]
- Hyperbolic caching [2]
- ARC [3]
- GreedyDualSize [4]

[1] L. A. Belady. 1966. A Study of Replacement Algorithms for a Virtual-storage Computer. IBM Syst. J. 5, 2 (June 1966), 78-101. DOI=http://dx.doi.org/10.1147/sj.52.0078
[2] Aaron Blankstein, Siddhartha Sen, and Michael J. Freedman. 2017. Hyperbolic caching: flexible caching for web applications. In Proceedings of the 2017 USENIX Conference on Usenix Annual Technical Conference (USENIX ATC '17). USENIX Association, Berkeley, CA, USA, 499-511.
[3] Nimrod Megiddo and Dharmendra S. Modha. 2003. ARC: A Self-Tuning, Low Overhead Replacement Cache. In Proceedings of the 2nd USENIX Conference on File and Storage Technologies (FAST '03). USENIX Association, Berkeley, CA, USA, 115-130.
[4] N. Young. The k-server dual and loose competitiveness for paging. Algorithmica, June 1994, vol. 11,(no.6):525-41. Rewritten version of ''On-line caching as cache size varies'', in The 2nd Annual ACM-SIAM Symposium on Discrete Algorithms, 241-250, 1991.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5644

Differential Revision: D16548817

Pulled By: HaoyuHuang

fbshipit-source-id: 838f76db9179f07911abaab46c97e1c929cfcd63
main
haoyuhuang 5 years ago committed by Facebook Github Bot
parent d150e01474
commit 6e78fe3c8d
  1. 1552
      tools/block_cache_analyzer/block_cache_pysim.py
  2. 80
      tools/block_cache_analyzer/block_cache_pysim.sh
  3. 476
      tools/block_cache_analyzer/block_cache_pysim_test.py
  4. 162
      tools/block_cache_analyzer/block_cache_trace_analyzer.cc
  5. 14
      tools/block_cache_analyzer/block_cache_trace_analyzer.h
  6. 31
      tools/block_cache_analyzer/block_cache_trace_analyzer_test.cc
  7. 35
      trace_replay/block_cache_tracer.cc
  8. 9
      trace_replay/block_cache_tracer.h
  9. 26
      utilities/simulator_cache/cache_simulator_test.cc

File diff suppressed because it is too large Load Diff

@ -10,6 +10,10 @@
# warmup_seconds: The number of seconds used for warmup.
# max_jobs: The max number of concurrent pysims to run.
# Install required packages to run simulations.
# sudo dnf install -y numpy scipy python-matplotlib ipython python-pandas sympy python-nose atlas-devel
ulimit -c 0
if [ $# -ne 5 ]; then
echo "Usage: ./block_cache_pysim.sh trace_file_path result_dir downsample_size warmup_seconds max_jobs"
exit 0
@ -20,17 +24,26 @@ result_dir="$2"
downsample_size="$3"
warmup_seconds="$4"
max_jobs="$5"
current_jobs=0
max_num_accesses=100000000
current_jobs=1
ml_tmp_result_dir="$result_dir/ml"
rm -rf "$ml_tmp_result_dir"
mkdir -p "$result_dir"
mkdir -p "$ml_tmp_result_dir"
for cache_type in "ts" "linucb" "ts_hybrid" "linucb_hybrid"
# Report miss ratio in the trace.
current_jobs=$(ps aux | grep pysim | grep python | grep -cv grep)
for cf_name in "all"
do
for cache_size in "1G" "2G" "4G" "8G" "16G" #"12G" "16G" "1T"
do
for cache_size in "16M" "256M" "1G" "2G" "4G" "8G" "12G" "16G"
for cache_type in "opt" "lru" "pylru" "pycctbbt" "pyhb" "ts" "trace" "lru_hybrid" #"pycctblevelbt" #"lru_hybridn" "opt" #"pylru" "pylru_hybrid" "pycctbbt" "pycccfbt" "trace"
do
if [[ $cache_type == "trace" && $cache_size != "16G" ]]; then
# We only need to collect miss ratios observed in the trace once.
continue
fi
while [ "$current_jobs" -ge "$max_jobs" ]
do
sleep 10
@ -38,12 +51,13 @@ do
current_jobs=$(ps aux | grep pysim | grep python | grep -cv grep)
echo "Waiting jobs to complete. Number of running jobs: $current_jobs"
done
output="log-ml-$cache_type-$cache_size"
echo "Running simulation for $cache_type and cache size $cache_size. Number of running jobs: $current_jobs. "
nohup python block_cache_pysim.py "$cache_type" "$cache_size" "$downsample_size" "$warmup_seconds" "$trace_file" "$ml_tmp_result_dir" >& $ml_tmp_result_dir/$output &
output="log-ml-$cache_type-$cache_size-$cf_name"
echo "Running simulation for $cache_type, cache size $cache_size, and cf_name $cf_name. Number of running jobs: $current_jobs. "
nohup python block_cache_pysim.py "$cache_type" "$cache_size" "$downsample_size" "$warmup_seconds" "$trace_file" "$ml_tmp_result_dir" "$max_num_accesses" "$cf_name" >& "$ml_tmp_result_dir/$output" &
current_jobs=$((current_jobs+1))
done
done
done
# Wait for all jobs to complete.
while [ $current_jobs -gt 0 ]
@ -57,14 +71,14 @@ done
echo "Combine individual pysim output files"
rm -rf "$result_dir/ml_*"
mrc_file="$result_dir/ml_mrc"
for header in "header-" "data-"
do
for fn in $ml_tmp_result_dir/*
for fn in "$ml_tmp_result_dir"/*
do
sum_file=""
time_unit=""
capacity=""
target_cf_name=""
if [[ $fn == *"timeline"* ]]; then
tmpfn="$fn"
IFS='-' read -ra elements <<< "$tmpfn"
@ -79,24 +93,43 @@ do
done
time_unit_index=$((time_unit_index+1))
capacity_index=$((time_unit_index+2))
target_cf_name_index=$((time_unit_index+3))
time_unit="${elements[$time_unit_index]}_"
capacity="${elements[$capacity_index]}_"
target_cf_name="${elements[$target_cf_name_index]}_"
fi
if [[ $fn == "${header}ml-policy-timeline"* ]]; then
sum_file="$result_dir/ml_${capacity}${time_unit}policy_timeline"
if [[ $fn == *"${header}ml-policy-timeline"* ]]; then
sum_file="$result_dir/ml_${target_cf_name}${capacity}${time_unit}policy_timeline"
fi
if [[ $fn == "${header}ml-policy-ratio-timeline"* ]]; then
sum_file="$result_dir/ml_${capacity}${time_unit}policy_ratio_timeline"
if [[ $fn == *"${header}ml-policy-ratio-timeline"* ]]; then
sum_file="$result_dir/ml_${target_cf_name}${capacity}${time_unit}policy_ratio_timeline"
fi
if [[ $fn == "${header}ml-miss-timeline"* ]]; then
sum_file="$result_dir/ml_${capacity}${time_unit}miss_timeline"
if [[ $fn == *"${header}ml-miss-timeline"* ]]; then
sum_file="$result_dir/ml_${target_cf_name}${capacity}${time_unit}miss_timeline"
fi
if [[ $fn == "${header}ml-miss-ratio-timeline"* ]]; then
sum_file="$result_dir/ml_${capacity}${time_unit}miss_ratio_timeline"
if [[ $fn == *"${header}ml-miss-ratio-timeline"* ]]; then
sum_file="$result_dir/ml_${target_cf_name}${capacity}${time_unit}miss_ratio_timeline"
fi
if [[ $fn == "${header}ml-mrc"* ]]; then
sum_file="$mrc_file"
if [[ $fn == *"${header}ml-mrc"* ]]; then
tmpfn="$fn"
IFS='-' read -ra elements <<< "$tmpfn"
target_cf_name=${elements[-1]}
sum_file="${result_dir}/ml_${target_cf_name}_mrc"
fi
if [[ $fn == *"${header}ml-avgmb"* ]]; then
tmpfn="$fn"
IFS='-' read -ra elements <<< "$tmpfn"
time_unit=${elements[3]}
target_cf_name=${elements[-1]}
sum_file="${result_dir}/ml_${time_unit}_${target_cf_name}_avgmb"
fi
if [[ $fn == *"${header}ml-p95mb"* ]]; then
tmpfn="$fn"
IFS='-' read -ra elements <<< "$tmpfn"
time_unit=${elements[3]}
target_cf_name=${elements[-1]}
sum_file="${result_dir}/ml_${time_unit}_${target_cf_name}_p95mb"
fi
if [[ $sum_file == "" ]]; then
continue
@ -106,13 +139,18 @@ do
continue
fi
fi
cat "$ml_tmp_result_dir/$fn" >> "$sum_file"
cat "$fn" >> "$sum_file"
done
done
echo "Done"
for fn in $result_dir/*
do
if [[ $fn == *"_mrc" || $fn == *"_avgmb" || $fn == *"_p95mb" ]]; then
# Sort MRC file by cache_type and cache_size.
tmp_file="$result_dir/tmp_mrc"
cat "$mrc_file" | sort -t ',' -k1,1 -k4,4n > "$tmp_file"
cat "$tmp_file" > "$mrc_file"
cat "$fn" | sort -t ',' -k1,1 -k4,4n > "$tmp_file"
cat "$tmp_file" > "$fn"
rm -rf "$tmp_file"
fi
done

@ -1,17 +1,30 @@
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
import random
import sys
from block_cache_pysim import (
ARCCache,
CacheEntry,
GDSizeCache,
HashTable,
HyperbolicPolicy,
LFUPolicy,
LinUCBCache,
LRUCache,
LRUPolicy,
MRUPolicy,
OPTCache,
OPTCacheEntry,
ThompsonSamplingCache,
TraceCache,
TraceRecord,
create_cache,
kMicrosInSecond,
kSampleSize,
run,
)
@ -33,30 +46,44 @@ def test_hash_table():
records = 100
for i in range(n):
key_id = random.randint(0, records)
v = random.randint(0, records)
key = "k{}".format(key_id)
value = "v{}".format(key_id)
action = random.randint(0, 2)
# print "{}:{}:{}".format(action, key, value)
value = CacheEntry(v, v, v, v, v, v, v)
action = random.randint(0, 10)
assert len(truth_map) == table.elements, "{} {} {}".format(
len(truth_map), table.elements, i
)
if action == 0:
table.insert(key, key_id, value)
truth_map[key] = value
elif action == 1:
if action <= 8:
if key in truth_map:
assert table.lookup(key, key_id) is not None
assert truth_map[key] == table.lookup(key, key_id)
assert truth_map[key].value_size == table.lookup(key, key_id).value_size
else:
assert table.lookup(key, key_id) is None
table.insert(key, key_id, value)
truth_map[key] = value
else:
table.delete(key, key_id)
deleted = table.delete(key, key_id)
if deleted:
assert key in truth_map
if key in truth_map:
del truth_map[key]
# Check all keys are unique in the sample set.
for _i in range(10):
samples = table.random_sample(kSampleSize)
unique_keys = {}
for sample in samples:
unique_keys[sample.key] = True
assert len(samples) == len(unique_keys)
assert len(table) == len(truth_map)
for key in truth_map:
assert table.lookup(key, int(key[1:])) is not None
assert truth_map[key].value_size == table.lookup(key, int(key[1:])).value_size
print("Test hash table: Success")
def assert_metrics(cache, expected_value):
def assert_metrics(cache, expected_value, expected_value_size=1, custom_hashtable=True):
assert cache.used_size == expected_value[0], "Expected {}, Actual {}".format(
expected_value[0], cache.used_size
)
@ -70,24 +97,35 @@ def assert_metrics(cache, expected_value):
), "Expected {}, Actual {}".format(
expected_value[2], cache.miss_ratio_stats.num_misses
)
assert cache.table.elements == len(expected_value[3]) + len(
assert len(cache.table) == len(expected_value[3]) + len(
expected_value[4]
), "Expected {}, Actual {}".format(
len(expected_value[3]) + len(expected_value[4]), cache.table.elements
)
for expeceted_k in expected_value[3]:
if custom_hashtable:
val = cache.table.lookup("b{}".format(expeceted_k), expeceted_k)
assert val is not None
assert val.value_size == 1
else:
val = cache.table["b{}".format(expeceted_k)]
assert val is not None, "Expected {} Actual: Not Exist {}, Table: {}".format(
expeceted_k, expected_value, cache.table
)
assert val.value_size == expected_value_size
for expeceted_k in expected_value[4]:
val = cache.table.lookup("g{}".format(expeceted_k), expeceted_k)
if custom_hashtable:
val = cache.table.lookup("g0-{}".format(expeceted_k), expeceted_k)
else:
val = cache.table["g0-{}".format(expeceted_k)]
assert val is not None
assert val.value_size == 1
assert val.value_size == expected_value_size
# Access k1, k1, k2, k3, k3, k3, k4
def test_cache(policies, expected_value):
cache = ThompsonSamplingCache(3, False, policies)
# When k4 is inserted,
# LRU should evict k1.
# LFU should evict k2.
# MRU should evict k3.
def test_cache(cache, expected_value, custom_hashtable=True):
k1 = TraceRecord(
access_time=0,
block_id=1,
@ -103,6 +141,14 @@ def test_cache(policies, expected_value):
key_id=1,
kv_size=5,
is_hit=1,
referenced_key_exist_in_block=1,
num_keys_in_block=0,
table_id=0,
seq_number=0,
block_key_size=0,
key_size=0,
block_offset_in_file=0,
next_access_seq_no=0,
)
k2 = TraceRecord(
access_time=1,
@ -119,6 +165,14 @@ def test_cache(policies, expected_value):
key_id=1,
kv_size=5,
is_hit=1,
referenced_key_exist_in_block=1,
num_keys_in_block=0,
table_id=0,
seq_number=0,
block_key_size=0,
key_size=0,
block_offset_in_file=0,
next_access_seq_no=0,
)
k3 = TraceRecord(
access_time=2,
@ -135,6 +189,14 @@ def test_cache(policies, expected_value):
key_id=1,
kv_size=5,
is_hit=1,
referenced_key_exist_in_block=1,
num_keys_in_block=0,
table_id=0,
seq_number=0,
block_key_size=0,
key_size=0,
block_offset_in_file=0,
next_access_seq_no=0,
)
k4 = TraceRecord(
access_time=3,
@ -151,6 +213,14 @@ def test_cache(policies, expected_value):
key_id=1,
kv_size=5,
is_hit=1,
referenced_key_exist_in_block=1,
num_keys_in_block=0,
table_id=0,
seq_number=0,
block_key_size=0,
key_size=0,
block_offset_in_file=0,
next_access_seq_no=0,
)
sequence = [k1, k1, k2, k3, k3, k3]
index = 0
@ -167,20 +237,29 @@ def test_cache(policies, expected_value):
expected_values.append([3, 5, 3, [1, 2, 3], []])
# Access k3, hit.
expected_values.append([3, 6, 3, [1, 2, 3], []])
access_time = 0
for access in sequence:
access.access_time = access_time
cache.access(access)
assert_metrics(cache, expected_values[index])
assert_metrics(
cache,
expected_values[index],
expected_value_size=1,
custom_hashtable=custom_hashtable,
)
access_time += 1
index += 1
k4.access_time = access_time
cache.access(k4)
assert_metrics(cache, expected_value)
assert_metrics(
cache, expected_value, expected_value_size=1, custom_hashtable=custom_hashtable
)
def test_lru_cache():
def test_lru_cache(cache, custom_hashtable):
print("Test LRU cache")
policies = []
policies.append(LRUPolicy())
# Access k4, miss. evict k1
test_cache(policies, [3, 7, 4, [2, 3, 4], []])
test_cache(cache, [3, 7, 4, [2, 3, 4], []], custom_hashtable)
print("Test LRU cache: Success")
@ -189,7 +268,10 @@ def test_mru_cache():
policies = []
policies.append(MRUPolicy())
# Access k4, miss. evict k3
test_cache(policies, [3, 7, 4, [1, 2, 4], []])
test_cache(
ThompsonSamplingCache(3, False, policies, cost_class_label=None),
[3, 7, 4, [1, 2, 4], []],
)
print("Test MRU cache: Success")
@ -198,22 +280,36 @@ def test_lfu_cache():
policies = []
policies.append(LFUPolicy())
# Access k4, miss. evict k2
test_cache(policies, [3, 7, 4, [1, 3, 4], []])
test_cache(
ThompsonSamplingCache(3, False, policies, cost_class_label=None),
[3, 7, 4, [1, 3, 4], []],
)
print("Test LFU cache: Success")
def test_mix(cache):
print("Test Mix {} cache".format(cache.cache_name()))
n = 100000
records = 199
records = 100
block_size_table = {}
trace_num_misses = 0
for i in range(n):
key_id = random.randint(0, records)
vs = random.randint(0, 10)
now = i * kMicrosInSecond
block_size = vs
if key_id in block_size_table:
block_size = block_size_table[key_id]
else:
block_size_table[key_id] = block_size
is_hit = key_id % 2
if is_hit == 0:
trace_num_misses += 1
k = TraceRecord(
access_time=i,
access_time=now,
block_id=key_id,
block_type=1,
block_size=vs,
block_size=block_size,
cf_id=0,
cf_name="",
level=0,
@ -223,13 +319,117 @@ def test_mix(cache):
get_id=key_id,
key_id=key_id,
kv_size=5,
is_hit=1,
is_hit=is_hit,
referenced_key_exist_in_block=1,
num_keys_in_block=0,
table_id=0,
seq_number=0,
block_key_size=0,
key_size=0,
block_offset_in_file=0,
next_access_seq_no=vs,
)
cache.access(k)
assert cache.miss_ratio_stats.miss_ratio() > 0
if cache.cache_name() == "Trace":
assert cache.miss_ratio_stats.num_accesses == n
assert cache.miss_ratio_stats.num_misses == trace_num_misses
else:
assert cache.used_size <= cache.cache_size
all_values = cache.table.values()
cached_size = 0
for value in all_values:
cached_size += value.value_size
assert cached_size == cache.used_size, "Expeced {} Actual {}".format(
cache.used_size, cached_size
)
print("Test Mix {} cache: Success".format(cache.cache_name()))
def test_end_to_end():
print("Test All caches")
n = 100000
nblocks = 1000
block_size = 16 * 1024
ncfs = 7
nlevels = 6
nfds = 100000
trace_file_path = "test_trace"
# All blocks are of the same size so that OPT must achieve the lowest miss
# ratio.
with open(trace_file_path, "w+") as trace_file:
access_records = ""
for i in range(n):
key_id = random.randint(0, nblocks)
cf_id = random.randint(0, ncfs)
level = random.randint(0, nlevels)
fd = random.randint(0, nfds)
now = i * kMicrosInSecond
access_record = ""
access_record += "{},".format(now)
access_record += "{},".format(key_id)
access_record += "{},".format(9) # block type
access_record += "{},".format(block_size) # block size
access_record += "{},".format(cf_id)
access_record += "cf_{},".format(cf_id)
access_record += "{},".format(level)
access_record += "{},".format(fd)
access_record += "{},".format(key_id % 3) # caller
access_record += "{},".format(0) # no insert
access_record += "{},".format(i) # get_id
access_record += "{},".format(i) # key_id
access_record += "{},".format(100) # kv_size
access_record += "{},".format(1) # is_hit
access_record += "{},".format(1) # referenced_key_exist_in_block
access_record += "{},".format(10) # num_keys_in_block
access_record += "{},".format(1) # table_id
access_record += "{},".format(0) # seq_number
access_record += "{},".format(10) # block key size
access_record += "{},".format(20) # key size
access_record += "{},".format(0) # block offset
access_record = access_record[:-1]
access_records += access_record + "\n"
trace_file.write(access_records)
print("Test All caches: Start testing caches")
cache_size = block_size * nblocks / 10
downsample_size = 1
cache_ms = {}
for cache_type in [
"ts",
"opt",
"lru",
"pylru",
"linucb",
"gdsize",
"pyccbt",
"pycctbbt",
]:
cache = create_cache(cache_type, cache_size, downsample_size)
run(trace_file_path, cache_type, cache, 0, -1, "all")
cache_ms[cache_type] = cache
assert cache.miss_ratio_stats.num_accesses == n
for cache_type in cache_ms:
cache = cache_ms[cache_type]
ms = cache.miss_ratio_stats.miss_ratio()
assert ms <= 100.0 and ms >= 0.0
# OPT should perform the best.
assert cache_ms["opt"].miss_ratio_stats.miss_ratio() <= ms
assert cache.used_size <= cache.cache_size
all_values = cache.table.values()
cached_size = 0
for value in all_values:
cached_size += value.value_size
assert cached_size == cache.used_size, "Expeced {} Actual {}".format(
cache.used_size, cached_size
)
print("Test All {}: Success".format(cache.cache_name()))
os.remove(trace_file_path)
print("Test All: Success")
def test_hybrid(cache):
print("Test {} cache".format(cache.cache_name()))
k = TraceRecord(
@ -247,6 +447,14 @@ def test_hybrid(cache):
key_id=1,
kv_size=0, # no size.
is_hit=1,
referenced_key_exist_in_block=1,
num_keys_in_block=0,
table_id=0,
seq_number=0,
block_key_size=0,
key_size=0,
block_offset_in_file=0,
next_access_seq_no=0,
)
cache.access(k) # Expect a miss.
# used size, num accesses, num misses, hash table size, blocks, get keys.
@ -319,22 +527,208 @@ def test_hybrid(cache):
k.key_id = 4 # Same row key and should not be inserted again.
k.kv_size = 1
cache.access(k)
assert_metrics(cache, [16, 103, 99, [i for i in range(101 - kSampleSize, 101)], []])
assert_metrics(
cache, [kSampleSize, 103, 99, [i for i in range(101 - kSampleSize, 101)], []]
)
print("Test {} cache: Success".format(cache.cache_name()))
def test_opt_cache():
print("Test OPT cache")
cache = OPTCache(3)
# seq: 0, 1, 2, 3, 4, 5, 6, 7, 8
# key: k1, k2, k3, k4, k5, k6, k7, k1, k8
# next_access: 7, 19, 18, M, M, 17, 16, 25, M
k = TraceRecord(
access_time=0,
block_id=1,
block_type=1,
block_size=1,
cf_id=0,
cf_name="",
level=0,
fd=0,
caller=1,
no_insert=0,
get_id=1, # the first get request.
key_id=1,
kv_size=0, # no size.
is_hit=1,
referenced_key_exist_in_block=1,
num_keys_in_block=0,
table_id=0,
seq_number=0,
block_key_size=0,
key_size=0,
block_offset_in_file=0,
next_access_seq_no=7,
)
cache.access(k)
assert_metrics(
cache, [1, 1, 1, [1], []], expected_value_size=1, custom_hashtable=False
)
k.access_time += 1
k.block_id = 2
k.next_access_seq_no = 19
cache.access(k)
assert_metrics(
cache, [2, 2, 2, [1, 2], []], expected_value_size=1, custom_hashtable=False
)
k.access_time += 1
k.block_id = 3
k.next_access_seq_no = 18
cache.access(k)
assert_metrics(
cache, [3, 3, 3, [1, 2, 3], []], expected_value_size=1, custom_hashtable=False
)
k.access_time += 1
k.block_id = 4
k.next_access_seq_no = sys.maxsize # Never accessed again.
cache.access(k)
# Evict 2 since its next access 19 is the furthest in the future.
assert_metrics(
cache, [3, 4, 4, [1, 3, 4], []], expected_value_size=1, custom_hashtable=False
)
k.access_time += 1
k.block_id = 5
k.next_access_seq_no = sys.maxsize # Never accessed again.
cache.access(k)
# Evict 4 since its next access MAXINT is the furthest in the future.
assert_metrics(
cache, [3, 5, 5, [1, 3, 5], []], expected_value_size=1, custom_hashtable=False
)
k.access_time += 1
k.block_id = 6
k.next_access_seq_no = 17
cache.access(k)
# Evict 5 since its next access MAXINT is the furthest in the future.
assert_metrics(
cache, [3, 6, 6, [1, 3, 6], []], expected_value_size=1, custom_hashtable=False
)
k.access_time += 1
k.block_id = 7
k.next_access_seq_no = 16
cache.access(k)
# Evict 3 since its next access 18 is the furthest in the future.
assert_metrics(
cache, [3, 7, 7, [1, 6, 7], []], expected_value_size=1, custom_hashtable=False
)
k.access_time += 1
k.block_id = 1
k.next_access_seq_no = 25
cache.access(k)
assert_metrics(
cache, [3, 8, 7, [1, 6, 7], []], expected_value_size=1, custom_hashtable=False
)
k.access_time += 1
k.block_id = 8
k.next_access_seq_no = sys.maxsize
cache.access(k)
# Evict 1 since its next access 25 is the furthest in the future.
assert_metrics(
cache, [3, 9, 8, [6, 7, 8], []], expected_value_size=1, custom_hashtable=False
)
# Insert a large kv pair to evict all keys.
k.access_time += 1
k.block_id = 10
k.block_size = 3
k.next_access_seq_no = sys.maxsize
cache.access(k)
assert_metrics(
cache, [3, 10, 9, [10], []], expected_value_size=3, custom_hashtable=False
)
print("Test OPT cache: Success")
def test_trace_cache():
print("Test trace cache")
cache = TraceCache(0)
k = TraceRecord(
access_time=0,
block_id=1,
block_type=1,
block_size=1,
cf_id=0,
cf_name="",
level=0,
fd=0,
caller=1,
no_insert=0,
get_id=1,
key_id=1,
kv_size=0,
is_hit=1,
referenced_key_exist_in_block=1,
num_keys_in_block=0,
table_id=0,
seq_number=0,
block_key_size=0,
key_size=0,
block_offset_in_file=0,
next_access_seq_no=7,
)
cache.access(k)
assert cache.miss_ratio_stats.num_accesses == 1
assert cache.miss_ratio_stats.num_misses == 0
k.is_hit = 0
cache.access(k)
assert cache.miss_ratio_stats.num_accesses == 2
assert cache.miss_ratio_stats.num_misses == 1
print("Test trace cache: Success")
if __name__ == "__main__":
policies = []
policies.append(MRUPolicy())
policies.append(LRUPolicy())
policies.append(LFUPolicy())
test_hash_table()
test_lru_cache()
test_trace_cache()
test_opt_cache()
test_lru_cache(
ThompsonSamplingCache(
3, enable_cache_row_key=0, policies=[LRUPolicy()], cost_class_label=None
),
custom_hashtable=True,
)
test_lru_cache(LRUCache(3, enable_cache_row_key=0), custom_hashtable=False)
test_mru_cache()
test_lfu_cache()
test_mix(ThompsonSamplingCache(100, False, policies))
test_mix(ThompsonSamplingCache(100, True, policies))
test_mix(LinUCBCache(100, False, policies))
test_mix(LinUCBCache(100, True, policies))
test_hybrid(ThompsonSamplingCache(kSampleSize, True, [LRUPolicy()]))
test_hybrid(LinUCBCache(kSampleSize, True, [LRUPolicy()]))
test_hybrid(
ThompsonSamplingCache(
kSampleSize,
enable_cache_row_key=1,
policies=[LRUPolicy()],
cost_class_label=None,
)
)
test_hybrid(
LinUCBCache(
kSampleSize,
enable_cache_row_key=1,
policies=[LRUPolicy()],
cost_class_label=None,
)
)
for cache_type in [
"ts",
"opt",
"arc",
"pylfu",
"pymru",
"trace",
"pyhb",
"lru",
"pylru",
"linucb",
"gdsize",
"pycctbbt",
"pycctb",
"pyccbt",
]:
for enable_row_cache in [0, 1, 2]:
cache_type_str = cache_type
if cache_type != "opt" and cache_type != "trace":
if enable_row_cache == 1:
cache_type_str += "_hybrid"
elif enable_row_cache == 2:
cache_type_str += "_hybridn"
test_mix(create_cache(cache_type_str, cache_size=100, downsample_size=1))
test_end_to_end()

@ -127,6 +127,9 @@ DEFINE_string(analyze_get_spatial_locality_labels, "",
"Group data blocks using these labels.");
DEFINE_string(analyze_get_spatial_locality_buckets, "",
"Group data blocks by their statistics using these buckets.");
DEFINE_string(skew_labels, "",
"Group the access count of a block using these labels.");
DEFINE_string(skew_buckets, "", "Group the skew labels using these buckets.");
DEFINE_bool(mrc_only, false,
"Evaluate alternative cache policies only. When this flag is true, "
"the analyzer does NOT maintain states of each block in memory for "
@ -147,6 +150,7 @@ namespace {
const std::string kMissRatioCurveFileName = "mrc";
const std::string kGroupbyBlock = "block";
const std::string kGroupbyTable = "table";
const std::string kGroupbyColumnFamily = "cf";
const std::string kGroupbySSTFile = "sst";
const std::string kGroupbyBlockType = "bt";
@ -164,6 +168,7 @@ const std::string kSupportedCacheNames =
// The suffix for the generated csv files.
const std::string kFileNameSuffixMissRatioTimeline = "miss_ratio_timeline";
const std::string kFileNameSuffixMissTimeline = "miss_timeline";
const std::string kFileNameSuffixSkew = "skewness";
const std::string kFileNameSuffixAccessTimeline = "access_timeline";
const std::string kFileNameSuffixCorrelation = "correlation_input";
const std::string kFileNameSuffixAvgReuseIntervalNaccesses =
@ -540,6 +545,62 @@ void BlockCacheTraceAnalyzer::WriteMissTimeline(uint64_t time_unit) const {
}
}
void BlockCacheTraceAnalyzer::WriteSkewness(
const std::string& label_str, const std::vector<uint64_t>& percent_buckets,
TraceType target_block_type) const {
std::set<std::string> labels = ParseLabelStr(label_str);
std::map<std::string, uint64_t> label_naccesses;
uint64_t total_naccesses = 0;
auto block_callback = [&](const std::string& cf_name, uint64_t fd,
uint32_t level, TraceType type,
const std::string& /*block_key*/, uint64_t block_id,
const BlockAccessInfo& block) {
if (target_block_type != TraceType::kTraceMax &&
target_block_type != type) {
return;
}
const std::string label = BuildLabel(
labels, cf_name, fd, level, type,
TableReaderCaller::kMaxBlockCacheLookupCaller, block_id, block);
label_naccesses[label] += block.num_accesses;
total_naccesses += block.num_accesses;
};
TraverseBlocks(block_callback, &labels);
std::map<std::string, std::map<uint64_t, uint64_t>> label_bucket_naccesses;
std::vector<std::pair<std::string, uint64_t>> pairs;
for (auto const& itr : label_naccesses) {
pairs.push_back(itr);
}
// Sort in descending order.
sort(
pairs.begin(), pairs.end(),
[=](std::pair<std::string, uint64_t>& a,
std::pair<std::string, uint64_t>& b) { return b.second < a.second; });
size_t prev_start_index = 0;
for (auto const& percent : percent_buckets) {
label_bucket_naccesses[label_str][percent] = 0;
size_t end_index = 0;
if (percent == port::kMaxUint64) {
end_index = label_naccesses.size();
} else {
end_index = percent * label_naccesses.size() / 100;
}
for (size_t i = prev_start_index; i < end_index; i++) {
label_bucket_naccesses[label_str][percent] += pairs[i].second;
}
prev_start_index = end_index;
}
std::string filename_suffix;
if (target_block_type != TraceType::kTraceMax) {
filename_suffix = block_type_to_string(target_block_type);
filename_suffix += "_";
}
filename_suffix += kFileNameSuffixSkew;
WriteStatsToFile(label_str, percent_buckets, filename_suffix,
label_bucket_naccesses, total_naccesses);
}
void BlockCacheTraceAnalyzer::WriteCorrelationFeatures(
const std::string& label_str, uint32_t max_number_of_values) const {
std::set<std::string> labels = ParseLabelStr(label_str);
@ -549,12 +610,16 @@ void BlockCacheTraceAnalyzer::WriteCorrelationFeatures(
[&](const std::string& cf_name, uint64_t fd, uint32_t level,
TraceType block_type, const std::string& /*block_key*/,
uint64_t /*block_key_id*/, const BlockAccessInfo& block) {
if (block.table_id == 0 && labels.find(kGroupbyTable) != labels.end()) {
// We only know table id information for get requests.
return;
}
if (labels.find(kGroupbyCaller) != labels.end()) {
// Group by caller.
for (auto const& caller_map : block.caller_access_timeline) {
const std::string label =
BuildLabel(labels, cf_name, fd, level, block_type,
caller_map.first, /*block_id=*/0);
caller_map.first, /*block_id=*/0, block);
auto it = block.caller_access_sequence__number_timeline.find(
caller_map.first);
assert(it != block.caller_access_sequence__number_timeline.end());
@ -563,14 +628,15 @@ void BlockCacheTraceAnalyzer::WriteCorrelationFeatures(
}
return;
}
const std::string label = BuildLabel(
labels, cf_name, fd, level, block_type,
TableReaderCaller::kMaxBlockCacheLookupCaller, /*block_id=*/0);
const std::string label =
BuildLabel(labels, cf_name, fd, level, block_type,
TableReaderCaller::kMaxBlockCacheLookupCaller,
/*block_id=*/0, block);
UpdateFeatureVectors(block.access_sequence_number_timeline,
block.access_timeline, label, &label_features,
&label_predictions);
};
TraverseBlocks(block_callback);
TraverseBlocks(block_callback, &labels);
WriteCorrelationFeaturesToFile(label_str, label_features, label_predictions,
max_number_of_values);
}
@ -656,7 +722,7 @@ std::set<std::string> BlockCacheTraceAnalyzer::ParseLabelStr(
std::string BlockCacheTraceAnalyzer::BuildLabel(
const std::set<std::string>& labels, const std::string& cf_name,
uint64_t fd, uint32_t level, TraceType type, TableReaderCaller caller,
uint64_t block_key) const {
uint64_t block_key, const BlockAccessInfo& block) const {
std::map<std::string, std::string> label_value_map;
label_value_map[kGroupbyAll] = kGroupbyAll;
label_value_map[kGroupbyLevel] = std::to_string(level);
@ -665,6 +731,7 @@ std::string BlockCacheTraceAnalyzer::BuildLabel(
label_value_map[kGroupbyBlockType] = block_type_to_string(type);
label_value_map[kGroupbyColumnFamily] = cf_name;
label_value_map[kGroupbyBlock] = std::to_string(block_key);
label_value_map[kGroupbyTable] = std::to_string(block.table_id);
// Concatenate the label values.
std::string label;
for (auto const& l : labels) {
@ -683,7 +750,8 @@ void BlockCacheTraceAnalyzer::TraverseBlocks(
const std::string& /*block_key*/,
uint64_t /*block_key_id*/,
const BlockAccessInfo& /*block_access_info*/)>
block_callback) const {
block_callback,
std::set<std::string>* labels) const {
for (auto const& cf_aggregates : cf_aggregates_map_) {
// Stats per column family.
const std::string& cf_name = cf_aggregates.first;
@ -698,6 +766,11 @@ void BlockCacheTraceAnalyzer::TraverseBlocks(
for (auto const& block_access_info :
block_type_aggregates.second.block_access_info_map) {
// Stats per block.
if (labels && block_access_info.second.table_id == 0 &&
labels->find(kGroupbyTable) != labels->end()) {
// We only know table id information for get requests.
return;
}
block_callback(cf_name, fd, level, type, block_access_info.first,
block_access_info.second.block_id,
block_access_info.second);
@ -733,7 +806,7 @@ void BlockCacheTraceAnalyzer::WriteGetSpatialLocality(
}
const std::string label =
BuildLabel(labels, cf_name, fd, level, TraceType::kBlockTraceDataBlock,
TableReaderCaller::kUserGet, /*block_id=*/0);
TableReaderCaller::kUserGet, /*block_id=*/0, block);
const uint64_t percent_referenced_for_existing_keys =
static_cast<uint64_t>(std::max(
@ -761,7 +834,7 @@ void BlockCacheTraceAnalyzer::WriteGetSpatialLocality(
->second += 1;
nblocks += 1;
};
TraverseBlocks(block_callback);
TraverseBlocks(block_callback, &labels);
WriteStatsToFile(label_str, percent_buckets, kFileNameSuffixPercentRefKeys,
label_pnrefkeys_nblocks, nblocks);
WriteStatsToFile(label_str, percent_buckets,
@ -792,7 +865,7 @@ void BlockCacheTraceAnalyzer::WriteAccessTimeline(const std::string& label_str,
continue;
}
const std::string label =
BuildLabel(labels, cf_name, fd, level, type, caller, block_id);
BuildLabel(labels, cf_name, fd, level, type, caller, block_id, block);
for (auto const& naccess : timeline.second) {
const uint64_t timestamp = naccess.first / time_unit;
const uint64_t num = naccess.second;
@ -806,7 +879,7 @@ void BlockCacheTraceAnalyzer::WriteAccessTimeline(const std::string& label_str,
access_count_block_id_map[naccesses].push_back(std::to_string(block_id));
}
};
TraverseBlocks(block_callback);
TraverseBlocks(block_callback, &labels);
// We have label_access_timeline now. Write them into a file.
const std::string user_access_prefix =
@ -877,9 +950,9 @@ void BlockCacheTraceAnalyzer::WriteReuseDistance(
uint32_t level, TraceType type,
const std::string& /*block_key*/, uint64_t block_id,
const BlockAccessInfo& block) {
const std::string label =
BuildLabel(labels, cf_name, fd, level, type,
TableReaderCaller::kMaxBlockCacheLookupCaller, block_id);
const std::string label = BuildLabel(
labels, cf_name, fd, level, type,
TableReaderCaller::kMaxBlockCacheLookupCaller, block_id, block);
if (label_distance_num_reuses.find(label) ==
label_distance_num_reuses.end()) {
// The first time we encounter this label.
@ -894,7 +967,7 @@ void BlockCacheTraceAnalyzer::WriteReuseDistance(
total_num_reuses += reuse_distance.second;
}
};
TraverseBlocks(block_callback);
TraverseBlocks(block_callback, &labels);
// We have label_naccesses and label_distance_num_reuses now. Write them into
// a file.
const std::string output_path =
@ -1016,17 +1089,17 @@ void BlockCacheTraceAnalyzer::WriteReuseInterval(
if (labels.find(kGroupbyCaller) != labels.end()) {
for (auto const& timeline : block.caller_num_accesses_timeline) {
const TableReaderCaller caller = timeline.first;
const std::string label =
BuildLabel(labels, cf_name, fd, level, type, caller, block_id);
const std::string label = BuildLabel(labels, cf_name, fd, level, type,
caller, block_id, block);
UpdateReuseIntervalStats(label, time_buckets, timeline.second,
&label_time_num_reuses, &total_num_reuses);
}
return;
}
// Does not group by caller so we need to flatten the access timeline.
const std::string label =
BuildLabel(labels, cf_name, fd, level, type,
TableReaderCaller::kMaxBlockCacheLookupCaller, block_id);
const std::string label = BuildLabel(
labels, cf_name, fd, level, type,
TableReaderCaller::kMaxBlockCacheLookupCaller, block_id, block);
std::map<uint64_t, uint64_t> timeline;
for (auto const& caller_timeline : block.caller_num_accesses_timeline) {
for (auto const& time_naccess : caller_timeline.second) {
@ -1045,7 +1118,7 @@ void BlockCacheTraceAnalyzer::WriteReuseInterval(
label_avg_reuse_naccesses[label].upper_bound(avg_reuse_interval)->second +=
block.num_accesses;
};
TraverseBlocks(block_callback);
TraverseBlocks(block_callback, &labels);
// Write the stats into files.
WriteStatsToFile(label_str, time_buckets, kFileNameSuffixReuseInterval,
@ -1074,9 +1147,9 @@ void BlockCacheTraceAnalyzer::WriteReuseLifetime(
} else {
lifetime = port::kMaxUint64 - 1;
}
const std::string label =
BuildLabel(labels, cf_name, fd, level, type,
TableReaderCaller::kMaxBlockCacheLookupCaller, block_id);
const std::string label = BuildLabel(
labels, cf_name, fd, level, type,
TableReaderCaller::kMaxBlockCacheLookupCaller, block_id, block);
if (label_lifetime_nblocks.find(label) == label_lifetime_nblocks.end()) {
// The first time we encounter this label.
@ -1087,7 +1160,7 @@ void BlockCacheTraceAnalyzer::WriteReuseLifetime(
label_lifetime_nblocks[label].upper_bound(lifetime)->second += 1;
total_nblocks += 1;
};
TraverseBlocks(block_callback);
TraverseBlocks(block_callback, &labels);
WriteStatsToFile(label_str, time_buckets, kFileNameSuffixReuseLifetime,
label_lifetime_nblocks, total_nblocks);
}
@ -1396,11 +1469,17 @@ Status BlockCacheTraceAnalyzer::WriteHumanReadableTraceRecord(
int ret = snprintf(
trace_record_buffer_, sizeof(trace_record_buffer_),
"%" PRIu64 ",%" PRIu64 ",%u,%" PRIu64 ",%" PRIu64 ",%s,%" PRIu32
",%" PRIu64 ",%u,%u,%" PRIu64 ",%" PRIu64 ",%" PRIu64 ",%u\n",
",%" PRIu64 ",%u,%u,%" PRIu64 ",%" PRIu64 ",%" PRIu64 ",%u,%u,%" PRIu64
",%" PRIu64 ",%" PRIu64 ",%" PRIu64 ",%" PRIu64 ",%" PRIu64 "\n",
access.access_timestamp, block_id, access.block_type, access.block_size,
access.cf_id, access.cf_name.c_str(), access.level, access.sst_fd_number,
access.caller, access.no_insert, access.get_id, get_key_id,
access.referenced_data_size, access.is_cache_hit);
access.referenced_data_size, access.is_cache_hit,
access.referenced_key_exist_in_block, access.num_keys_in_block,
BlockCacheTraceHelper::GetTableId(access),
BlockCacheTraceHelper::GetSequenceNumber(access), access.block_key.size(),
access.referenced_key.size(),
BlockCacheTraceHelper::GetBlockOffsetInFile(access));
if (ret < 0) {
return Status::IOError("failed to format the output");
}
@ -1432,13 +1511,13 @@ Status BlockCacheTraceAnalyzer::RecordAccess(
uint64_t get_key_id = 0;
if (access.caller == TableReaderCaller::kUserGet &&
access.get_id != BlockCacheTraceHelper::kReservedGetId) {
std::string row_key = BlockCacheTraceHelper::ComputeRowKey(access);
if (get_key_info_map_.find(row_key) == get_key_info_map_.end()) {
get_key_info_map_[row_key].key_id = unique_get_key_id_;
get_key_id = unique_get_key_id_;
std::string user_key = ExtractUserKey(access.referenced_key).ToString();
if (get_key_info_map_.find(user_key) == get_key_info_map_.end()) {
get_key_info_map_[user_key].key_id = unique_get_key_id_;
unique_get_key_id_++;
}
get_key_info_map_[row_key].AddAccess(access, access_sequence_number_);
get_key_id = get_key_info_map_[user_key].key_id;
get_key_info_map_[user_key].AddAccess(access, access_sequence_number_);
}
if (compute_reuse_distance_) {
@ -2224,6 +2303,25 @@ int block_cache_trace_analyzer_tool(int argc, char** argv) {
analyzer.WriteCorrelationFeaturesForGet(
FLAGS_analyze_correlation_coefficients_max_number_of_values);
}
if (!FLAGS_skew_labels.empty() && !FLAGS_skew_buckets.empty()) {
std::vector<uint64_t> buckets = parse_buckets(FLAGS_skew_buckets);
std::stringstream ss(FLAGS_skew_labels);
while (ss.good()) {
std::string label;
getline(ss, label, ',');
if (label.find("block") != std::string::npos) {
analyzer.WriteSkewness(label, buckets,
TraceType::kBlockTraceIndexBlock);
analyzer.WriteSkewness(label, buckets,
TraceType::kBlockTraceFilterBlock);
analyzer.WriteSkewness(label, buckets, TraceType::kBlockTraceDataBlock);
analyzer.WriteSkewness(label, buckets, TraceType::kTraceMax);
} else {
analyzer.WriteSkewness(label, buckets, TraceType::kTraceMax);
}
}
}
return 0;
}

@ -33,6 +33,8 @@ struct GetKeyInfo {
// Statistics of a block.
struct BlockAccessInfo {
uint64_t block_id = 0;
uint64_t table_id = 0;
uint64_t block_offset = 0;
uint64_t num_accesses = 0;
uint64_t block_size = 0;
uint64_t first_access_time = 0;
@ -73,6 +75,8 @@ struct BlockAccessInfo {
if (first_access_time == 0) {
first_access_time = access.access_timestamp;
}
table_id = BlockCacheTraceHelper::GetTableId(access);
block_offset = BlockCacheTraceHelper::GetBlockOffsetInFile(access);
last_access_time = access.access_timestamp;
block_size = access.block_size;
caller_num_access_map[access.caller]++;
@ -301,6 +305,10 @@ class BlockCacheTraceAnalyzer {
void WriteCorrelationFeaturesForGet(uint32_t max_number_of_values) const;
void WriteSkewness(const std::string& label_str,
const std::vector<uint64_t>& percent_buckets,
TraceType target_block_type) const;
const std::map<std::string, ColumnFamilyAccessInfoAggregate>&
TEST_cf_aggregates_map() const {
return cf_aggregates_map_;
@ -312,7 +320,8 @@ class BlockCacheTraceAnalyzer {
std::string BuildLabel(const std::set<std::string>& labels,
const std::string& cf_name, uint64_t fd,
uint32_t level, TraceType type,
TableReaderCaller caller, uint64_t block_key) const;
TableReaderCaller caller, uint64_t block_key,
const BlockAccessInfo& block) const;
void ComputeReuseDistance(BlockAccessInfo* info) const;
@ -341,7 +350,8 @@ class BlockCacheTraceAnalyzer {
const std::string& /*block_key*/,
uint64_t /*block_key_id*/,
const BlockAccessInfo& /*block_access_info*/)>
block_callback) const;
block_callback,
std::set<std::string>* labels = nullptr) const;
void UpdateFeatureVectors(
const std::vector<uint64_t>& access_sequence_number_timeline,

@ -181,7 +181,9 @@ class BlockCacheTracerTest : public testing::Test {
analyze_get_spatial_locality_labels_,
"-analyze_get_spatial_locality_buckets=" +
analyze_get_spatial_locality_buckets_,
"-analyze_correlation_coefficients_labels=all"};
"-analyze_correlation_coefficients_labels=all",
"-skew_labels=all",
"-skew_buckets=10,50,100"};
char arg_buffer[kArgBufferSize];
char* argv[kMaxArgCount];
int argc = 0;
@ -331,6 +333,33 @@ TEST_F(BlockCacheTracerTest, BlockCacheAnalyzer) {
}
}
}
{
// Validate the skewness csv file.
const std::string skewness_file_path = test_path_ + "/all_skewness";
std::ifstream skew_file(skewness_file_path);
// Read header.
std::string line;
ASSERT_TRUE(getline(skew_file, line));
std::stringstream ss(line);
double sum_percent = 0;
while (getline(skew_file, line)) {
std::stringstream ss_naccess(line);
std::string substr;
bool read_label = false;
while (ss_naccess.good()) {
ASSERT_TRUE(getline(ss_naccess, substr, ','));
if (!read_label) {
read_label = true;
continue;
}
sum_percent += ParseDouble(substr);
}
}
ASSERT_EQ(100.0, sum_percent);
ASSERT_FALSE(getline(skew_file, line));
skew_file.close();
ASSERT_OK(env_->DeleteFile(skewness_file_path));
}
{
// Validate the timeline csv files.
const std::vector<std::string> time_units{"_60", "_3600"};

@ -61,11 +61,40 @@ std::string BlockCacheTraceHelper::ComputeRowKey(
return "";
}
Slice key = ExtractUserKey(access.referenced_key);
uint64_t seq_no = access.get_from_user_specified_snapshot == Boolean::kFalse
return std::to_string(access.sst_fd_number) + "_" + key.ToString();
}
uint64_t BlockCacheTraceHelper::GetTableId(
const BlockCacheTraceRecord& access) {
if (!IsGetOrMultiGet(access.caller) || access.referenced_key.size() < 4) {
return 0;
}
return static_cast<uint64_t>(DecodeFixed32(access.referenced_key.data())) + 1;
}
uint64_t BlockCacheTraceHelper::GetSequenceNumber(
const BlockCacheTraceRecord& access) {
if (!IsGetOrMultiGet(access.caller)) {
return 0;
}
return access.get_from_user_specified_snapshot == Boolean::kFalse
? 0
: 1 + GetInternalKeySeqno(access.referenced_key);
return std::to_string(access.sst_fd_number) + "_" + key.ToString() + "_" +
std::to_string(seq_no);
}
uint64_t BlockCacheTraceHelper::GetBlockOffsetInFile(
const BlockCacheTraceRecord& access) {
Slice input(access.block_key);
uint64_t offset = 0;
while (true) {
uint64_t tmp = 0;
if (GetVarint64(&input, &tmp)) {
offset = tmp;
} else {
break;
}
}
return offset;
}
BlockCacheTraceWriter::BlockCacheTraceWriter(

@ -31,6 +31,15 @@ class BlockCacheTraceHelper {
// Row key is a concatenation of the access's fd_number and the referenced
// user key.
static std::string ComputeRowKey(const BlockCacheTraceRecord& access);
// The first four bytes of the referenced key in a Get request is the table
// id.
static uint64_t GetTableId(const BlockCacheTraceRecord& access);
// The sequence number of a get request is the last part of the referenced
// key.
static uint64_t GetSequenceNumber(const BlockCacheTraceRecord& access);
// Block offset in a file is the last varint64 in the block key.
static uint64_t GetBlockOffsetInFile(const BlockCacheTraceRecord& access);
static const std::string kUnknownColumnFamilyName;
static const uint64_t kReservedGetId;
};

@ -84,7 +84,7 @@ class CacheSimulatorTest : public testing::Test {
for (auto const& key : keys) {
std::string row_key = kRefKeyPrefix + key + kRefKeySequenceNumber;
auto handle =
sim_cache->Lookup("0_" + ExtractUserKey(row_key).ToString() + "_0");
sim_cache->Lookup("0_" + ExtractUserKey(row_key).ToString());
EXPECT_NE(nullptr, handle);
sim_cache->Release(handle);
}
@ -229,10 +229,9 @@ TEST_F(CacheSimulatorTest, HybridRowBlockCacheSimulator) {
ASSERT_EQ(100, cache_simulator->miss_ratio_stats().miss_ratio());
ASSERT_EQ(10, cache_simulator->miss_ratio_stats().user_accesses());
ASSERT_EQ(100, cache_simulator->miss_ratio_stats().user_miss_ratio());
auto handle = sim_cache->Lookup(
std::to_string(first_get.sst_fd_number) + "_" +
ExtractUserKey(first_get.referenced_key).ToString() + "_" +
std::to_string(1 + GetInternalKeySeqno(first_get.referenced_key)));
auto handle =
sim_cache->Lookup(std::to_string(first_get.sst_fd_number) + "_" +
ExtractUserKey(first_get.referenced_key).ToString());
ASSERT_NE(nullptr, handle);
sim_cache->Release(handle);
for (uint32_t i = 100; i < block_id; i++) {
@ -256,10 +255,9 @@ TEST_F(CacheSimulatorTest, HybridRowBlockCacheSimulator) {
ASSERT_EQ(15, cache_simulator->miss_ratio_stats().user_accesses());
ASSERT_EQ(66, static_cast<uint64_t>(
cache_simulator->miss_ratio_stats().user_miss_ratio()));
handle = sim_cache->Lookup(
std::to_string(second_get.sst_fd_number) + "_" +
ExtractUserKey(second_get.referenced_key).ToString() + "_" +
std::to_string(1 + GetInternalKeySeqno(second_get.referenced_key)));
handle =
sim_cache->Lookup(std::to_string(second_get.sst_fd_number) + "_" +
ExtractUserKey(second_get.referenced_key).ToString());
ASSERT_NE(nullptr, handle);
sim_cache->Release(handle);
for (uint32_t i = 100; i < block_id; i++) {
@ -394,7 +392,7 @@ TEST_F(CacheSimulatorTest, HybridRowBlockCacheSimulatorGetTest) {
AssertCache(sim_cache, cache_simulator->miss_ratio_stats(), 7, 8, 4,
{"1", "2", "3", "5"}, {"1", "2", "4"});
for (auto const& key : {"1", "2", "4"}) {
auto handle = sim_cache->Lookup("0_" + kRefKeyPrefix + key + "_0");
auto handle = sim_cache->Lookup("0_" + kRefKeyPrefix + key);
ASSERT_NE(nullptr, handle);
sim_cache->Release(handle);
}
@ -417,7 +415,7 @@ TEST_F(CacheSimulatorTest, HybridRowBlockCacheSimulatorGetTest) {
AssertCache(sim_cache, cache_simulator->miss_ratio_stats(), 16, 103, 99, {},
{});
for (auto const& key : {"1", "2", "4"}) {
auto handle = sim_cache->Lookup("0_" + kRefKeyPrefix + key + "_0");
auto handle = sim_cache->Lookup("0_" + kRefKeyPrefix + key);
ASSERT_EQ(nullptr, handle);
}
}
@ -437,9 +435,9 @@ TEST_F(CacheSimulatorTest, HybridRowBlockNoInsertCacheSimulator) {
cache_simulator->Access(first_get);
block_id++;
}
auto handle = sim_cache->Lookup(
std::to_string(first_get.sst_fd_number) + "_" +
ExtractUserKey(first_get.referenced_key).ToString() + "_0");
auto handle =
sim_cache->Lookup(std::to_string(first_get.sst_fd_number) + "_" +
ExtractUserKey(first_get.referenced_key).ToString());
ASSERT_NE(nullptr, handle);
sim_cache->Release(handle);
// All blocks are missing from the cache since insert_blocks_row_kvpair_misses

Loading…
Cancel
Save