Thursday, December 2, 2021

Summarizing the different implementations of tiered compaction

This is my first attempt to summarize tiered compaction as implemented by RocksDB, Cassandra, ScyllaDB and HBase. I have limited experience with tiered compaction (that will change) and limited knowledge of Cassandra, ScyllaDB and HBase (that won't change soon) so I will make mistakes but enjoy the learning process. To keep this brief I ignore the variants optimizations for time-series and I don't explain compaction basics.

Different DBMS use different names for tiered compaction:

  • RocksDB has universal compaction
  • HBase has ExploringCompactionPolicy and RatioBasedCompactionPolicy, see here
  • Cassandra has STCS (Size Tiered Compaction Strategy)
  • ScyllaDB has STCS and ICS (Incremental Compaction Strategy), see here
Context

The workload matters a lot when considering compaction algorithms. There are many but I list only three below. Note that many real workloads are a mixture of patterns, an example is some overwrites, some inserts.
  • Overwrite-mostly - all of my time in LSM-land has been focused on overwrite-mostly workloads. Compaction is useful for overwrites because it reclaims space by removing tombstone and old versions.
  • Insert-mostly - for now I assume that something removes data via TTL or explicit deletes otherwise the database size grows forever. Insert-only can be further divided into:
    • Insert-only in key order - this is the easiest pattern to support. Bloom filters are less useful because pruning based on min/max keys per sorted run can eliminate many runs from being accessed on reads. Excluding implementation artifacts there are few benefits from using compaction in this case. While the sorted runs created by memtable flush will be small, they can be concatenated into larger, logical sorted runs that are range-partitioned although not all implementations do that.
    • Insert-only not in key order - compaction won't reclaim space (there is nothing to reclaim) but it still helps to reduce read-amplification.
STCS vs Prefix

While STCS is an accepted and good name for one approach to tiered compaction, HBase and RocksDB have a similar approach but use different names (RatioBasedCompactionPolicy, Universal). I prefer to call that Prefix compaction for reasons explained below.

While it would help to show examples (via diagrams or text) of the compactions that are done for STCS and Prefix assuming a sequence of memtable flushes, this post is already too long. I will do that in a future post.

Properties

Definitions:
  • By compaction picker I mean the algorithm that decides which sorted runs to compact.
  • Major compaction merges all sorted runs, minor compaction does not. 
  • The metadata for a sorted run includes the min and max keys stored in the sorted run and the min and max commit timestamp used by a key in that sorted run.
I prefer to have a geek code for tiered compaction implementations but I don't have the expertise yet to suggest that. Regardless, below is an incomplete list of things that distinguish the implementations:

  • What triggers major compaction?
    • Many implementations only do major compaction manually (when requested by an admin). The reasons for not doing it automatically include 1) it can take a long time and 2) there might be a transient 2X increase in space. Note that some implementations can merge the largest sorted runs without merging all sorted runs and this can cause the same problems as major compaction.
  • How are sorted runs ordered as viewed by the compaction picker?
    • They can be ordered by commit timestamp or size.
    • Commit timestamp order frequently implies size order, but sometimes explicit deletes, compaction removing a larger/older value in favor of a smaller/new value or TTL means that older sorted runs are not larger.
  • Does the compaction picker (almost) always consume a prefix of the sorted runs or can it consume a substring?
    • This assumes there is an order imposed on the sorted runs (see the previous question).
  • Can the compaction picker merge sorted runs that aren't adjacent in commit timestamp order?
    • See the two previous questions. If this is done the sorted runs can overlap with respect to commit timestamp ranges. A side-effect of allowing sorted runs to overlap in commit timestamp order might be that merging iterators can't use a short-circuit optimization for point queries -- reading from iterators in commit timestamp order and stopping on the first matching key or tombstone. I don't know if that side-effect is a big deal. 
  • What read-amplification constraints trigger minor compaction?
    • The trigger can be global or local. Global is triggered when there are too many sorted runs. Local is triggered when there are too many sorted runs within a size bucket.
    • These are naive because they assume all sorted runs might have to be read for a query. For workloads where the newly written keys have some correlation with time then the assumption might be wrong, many sorted runs can be pruned based on their min/max key and the number of sorted runs in the LSM tree is a too-pessimistic estimate of the read-amp. Time-series workload might break the naive assumption, but I am trying to not discuss LSM optimizations for time-series in this post.
  • Is there a minimum number of sorted runs to merge for minor compaction?
  • Is there read-triggered compaction?
    • LevelDB has a feature to trigger compaction for regions of the LSM tree with leveled compaction. Some tiered compaction implementations have a read hotness feature that prioritizes compaction for sorted runs that get more reads.
  • What space-amplification constraints trigger compaction?
    • This assumes there is an estimate for space-amp and there might not be one. If there is an estimate it is usually naive because a less naive estimate is expensive. The naive estimate is sizeof(all-sorted-runs) / sizeof(largest-sorted-run). I think this can be improved.
  • Do large sorted runs imply large filter and index blocks in memory?
    • If index and filter blocks are per sorted run, stored as a contiguous sequence of bytes and cannot be paged then a large sorted run implies there are large index and filter blocks. These are inefficient to cache and to read and decompress from storage. 
    • One alternative is to page them so they can be read from storage and cached in pieces. 
    • Another alternative is to have large logical sorted runs that are range partitioned into N sorted runs. Each of the N sorted runs has its own and smaller index and filter blocks.
  • Is compaction for large sorted runs incremental?
    • By incremental I mean there isn't a transient 2X increase in disk space during compaction and that disk space used by compaction input is reclaimed before a large compaction is complete.
  • Is compaction single-threaded?
    • Leveled compaction is usually single-thread because each compaction step is limited to a small amount of data. And then background threads can do many small compaction steps concurrently. But a compaction step can be much larger with tiered. Multi-threading can reduce this problem. 
    • One approach to multi-threading is to have N threads divide the compaction input into N key ranges and work on them independently.
    • Another approach to multi-threading is to have one thread do the merge, 1+ threads read and decompress compaction input and 1+ threads post-process (collect stats, create bloom filters, compress) compaction output. The separate threads for reading and writing can also do async read-ahead and write-behind.
  • Are there optimizations for inserts in key order?
  • What is the space-amp vs read-amp vs write-amp tradeoff?
    • Implementations have different tradeoffs for read, write and space amplification. I am not aware of an attempt to quantify the differences. My answer is left for a future blog post.

RocksDB

There are plans for improving universal compaction and some details are here.

Answers to the geek code questions:
  • The compaction picker is here and options are here
  • Major compaction is triggered automatically when the space-amp constraint is exceeded. That is set by the option max_size_amplification_percent
  • Sorted runs are ordered by commit timestamp (seqno)
  • The compaction picker usually picks a prefix of the sorted runs. But it skips the first N if they are currently being compacted so it doesn't always pick a prefix. The next sorted run is added to the prefix when sizeof(next run) <= sizeof(selected runs) X size_ratio. When the size_ratio check fails or max_merge_width is reached then the minor compaction input is known. The stop_style option can also be used but I won't describe that here.
  • Compaction input sorted runs are always adjacent in commit timestamp order.
  • The min and max number of sorted runs to merge are set by min_merge_width and max_merge_width
  • The read-amp constraint that triggers minor compaction is set by the option level0_file_num_compaction_trigger which is borrowed from leveled compaction. This is global.
  • There is no read-triggered compaction. While it was inherited from LevelDB it is currently disabled in RocksDB.
  • The space-amp constraint triggers major compaction as mentioned above. The naive estimate is used for space-amp. The estimate is truthy for overwrite-mostly workloads. I assume the space-amp constraint should be disabled for insert-mostly workloads.
  • Large sorted runs can lead to large index and filter blocks but that is unlikely because the largest sorted runs are logical and range-partitioned with an index and filter per partition (see here). RocksDB also optionally supports partitioned index and filter blocks.
  • Compaction for large sorted runs is not incremental (yet)
  • Large compactions are multi-threaded via subcompactions that split compaction input into N ranges (see here)
  • The allow_trivial_move option can use trivial moves in some cases when inserts are in key order to avoid re-writing sorted runs.
HBase

Answers to the geek code questions:
  • Compaction options are here. Search for hstore.compaction.
  • The compaction policies (ExploringCompactionPolicy, RatioBasedCompactionPolicy) are explained here.
  • The min and max number of sorted runs to merge are set by hbase.hstore.compaction.{min,max}
  • Time-based major compaction can be enabled or disabled. Search for hstore.hregion.majorcompaction
  • RatioBasedCompactionPolicy seems similar to universal compaction in RocksDB but ExploringCompactionPolicy might be closer to STCS. However, I didn't spend enough time to figure them out.
Cassandra

Answers to the geek code questions for STCS:
  • The compaction picker is here and options are here
  • Major compaction is triggered manually
  • Sorted runs are ordered by size and the compaction picker then groups them into size classes, then looks at size classes that have at least min_threshold sorted runs and chooses the one that gets the most reads (read hotness). The bucket_high and bucket_low options determine the range of sorted run sizes that can be in one size class (bucket). More detail is here and here.
  • Sorted runs that are not adjacent in commit timestamp order can be merged
  • The min and max number of sorted runs to merge are determined by the options min_threshold and max_threshold
  • The read-amp constraint is min_threshold and triggers minor compaction when there is a size class with at least that many sorted runs
  • There isn't support for read-triggered minor compaction but read hotness is used to prioritize which size class to compact next.
  • I am not sure whether large index and filter blocks are a problem
  • Compaction is single-threaded. I don't know whether the sharding built into Cassandra makes it easier to avoid large shards and the problems related to large compactions.
  • Does not do incremental compaction
  • I am not aware of optimizations for key-order inserts, similar to the allow_trivial_move feature in RocksDB universal compaction.
  • Cassandra might use HyperLogLog to estimate overlap between sorted runs. This can help estimate whether there would be a benefit from merging sorted runs by predicting how many redundant keys would be removed. But all I have today is a DataStax blog post.

ScyllaDB

Answers to the geek code questions for STCS (size tiered compaction strategy) with a few answers for ICS (incremental compaction strategy). Most of the answers above for Cassandra are also true here:
  • The compaction picker for STCS is here and options are here
  • Major compaction is triggered manually for STCS. ICS has an option to trigger it based on a SAG (space amplification goal). And they explain it is useful for overwrite workloads.
  • The min and max number of sorted runs to merge are determined by the options min_threshold and max_threshold
  • Compaction is single-threaded but ScyllaDB explains how the shard-per-core model reduces the problem from large/slow compactions.
  • STCS does not do incremental compaction but ScyllaDB Enterprise includes ICS
Updates
  • Added a link to the use of HyperLogLog by DataStax Cassandra to estimate sorted run overlap

Wednesday, December 1, 2021

Sysbench: MySQL 5.6, 5.7 & 8.0 on a small server

This has results for sysbench with MySQL versions 5.6.49, 5.7.35 and 8.0.2x using the same setup as previously used for the insert benchmark. The versions for 8.0.2x are 8.0.20, 8.0.22, 8.0.23, 8.0.26 and 8.0.27.

Executive summary:

  • For CPU-bound setups, MySQL 8.0.27 is slower than 5.6.49 on 39 of the 42 microbenchmarks. For the microbenchmarks when it was slower, on average it gets 72% of the throughput vs 5.6.49.
  • For IO-bound setups, MySQL 8.0.27 is slower than 5.6.49 on 27 of the 42 microbenchmarks, faster on 13 and had the same throughput on 2.
  • In most cases more CPU overhead in 8.0 is the reason it is slower than 5.6.

Details

See the Postgres post for more details on how I run sysbench. The summary is there are 43 invocations of sysbench and each is a microbenchmark. But a typo meant I only have results for 42 of the 43.

Three configurations were tested: 10M rows without prepared statements, 10M rows with prepared statements, 400M rows without prepared statements. The 10M row tests are CPU-bound as the database fits in memory. The 400M row test is usually IO-bound as the database is larger than memory. Below I call the test configurations 10m.prep0, 10m.prep1 and 400m.prep0. The database sizes are 2.9G at 10M rows and 91G at 400M rows after the initial load.

One of my goals was to understand the impact of using prepared statements with MySQL especially with respect to CPU overhead. They didn't make a big difference with the exception of the one test for which there is a performance bug with prepared statements (random-points.pre.range1000.pk1).

How to read the results

The presentation for these results is just text files pasted into gists. There are two types of files: qps and metrics. 

The qps file has the relative QPS for each test where the relative QPS is the ratio: QPS-for-me / QPS-for-base.

  • QPS-for-me is the QPS for one of MySQL 5.6.49, 5.7.35 or 8.0.2x
  • QPS-for-base is the QPS for MySQL 5.6.49
  • Thus each line in the qps file has seven numeric columns and the first one has the value 1.00 (QPS-for-5.6.49 / QPS-for-5.6.49). The second through seventh columns have the values for (QPS-for-me / QPS-for-5.6.49) where me is 5.7.35, 8.0.20, 8.0.22, 8.0.23, 8.0.26 and 8.0.27 in that order. When the value in a column is less than 1.0 then that version is slower than 5.6.49.
The metrics file has absolute and relative (to results for 5.6.49) values for each of the 42 tests. This file is long so I start with the qps file and then consult the metrics file to understand why one result is better than another.
  • cpu/o - CPU per operation, measured by iostat. While there is a unit for this, I don't worry about that as it is most useful for comparing numbers between different test configurations so the units drop out.
  • r/o - storage reads per operation, measured by iostat
  • rKB/o - KB read from storage per operation, measured by iostat
  • wKB/o - KB written to storage per operation,  measured by iostat
  • o/s - operations/second (QPS, inserts/s, etc). I tend to use QPS below for everything.
  • dbms - the MySQL version and configuration file
Results

Here are the qps and metrics files for the 10m.prep0 (CPU-bound, no prepared statements):
  • 8.0.27 is faster than 5.6.49 on 3 of 42 tests (read-only.pre.range10000,pk1, read-only.range10000.pk1, update-index.range100,pk1). The first two do long range scans and the relative QPS is 1.25. For the update-index test the relative QPS is 1.48.
  • The relative QPS for 8.0.27 is less than 0.8 for 35 of the 42 tests. This means that 8.0.27 gets less than 80% of the throughput relative to 5.6.49 on 35 of the tests. The minimum relative QPS is 0.61. For the 39 tests on which 8.0.27 is slower than 5.6.49 the average of the relative QPS is 0.72.
  • For the tests in which the relative QPS in 8.0.27 is much less than one the cause is using more CPU/query. See the cpu/o column in the metrics file.
  • The speedup for update-index is harder to explain. 8.0.2x uses less CPU, but the difference isn't large enough to explain the improvement. The speedup for the read-only tests is mostly from using less CPU/query in 8.0.2x. See the cpu/o column in the metrics file.

Here are the qps and metrics files for the 10m.prep1 (CPU-bound, prepared statements):
  • Results are similar to 10m.prep0
  • 8.0.27 is faster than 5.6.49 3 of 42 tests. The relative QPS is 1.24 for the read-only tests that do long range scans and 1.54 for the update-index test.
  • The relative QPS for 8.0.27 is less than 0.8 for 35 of the 42 tests. 
  • The minimum relative QPS is 0.30 for random-points.pre.range1000.pk1 (AFAIK there is a known perf bug in the optimizer). MySQL 8.0.2x uses more than 3X CPU/query compared to 5.6.49. See the cpu/o column in the metrics file. 
  • For the 39 tests on which 8.0.27 is slower than 5.6.49 the average of the relative QPS is 0.70.

Here are the qps and metrics files for the 400m.prep0 (IO-bound, no prepared statements):
  • 8.0.27 is faster than 5.6.49 for 13 of the 42 tests (tests are listed below)
  • 8.0.27 matched 5.6.49 QPS for 2 of the 42 tests (random-points.pre.range1000.pk1, random-points.range1000.pk1)
  • The relative QPS for 8.0.27 is less than 0.8 for 8 of the 42 tests (tests are listed below)
  • For the 27 tests on which 8.0.27 is slower than 5.6.49 the average of the relative QPS is 0.84

Relative QPS for 8.0.27 vs 5.6.49 when 8.0.27 is faster for the IO-bound tests:

1.22    point-query.pre.range100.pk1
1.02    random-points.pre.range10.pk1
1.05    read-only.pre.range100.pk1
1.13    read-only.pre.range10000.pk1
1.03    range-covered-pk.pre.range100.pk1
1.02    range-notcovered-pk.pre.range100.pk1
1.12    update-inlist.range100.pk1
1.17    read-only.range100.pk1
1.14    read-only.range10000.pk1
1.03    point-query.range100.pk1
1.05    random-points.range10.pk1
1.05    range-covered-pk.range100.pk1
1.06    range-notcovered-pk.range100.pk1

Relative QPS for 8.0.27 vs 5.6.49 when 8.0.27 is much slower for the IO-bound tests:

0.75    range-covered-si.pre.range100.pk1
0.68    update-one.range100.pk1
0.73    update-zipf.range100.pk1
0.55    read-write.range10.pk1
0.65    hot-points.range100.pk1
0.75    range-covered-si.range100.pk1
0.79    scan.range100.pk1
0.60    insert.range100.pk1









Sysbench: PostgreSQL 12, 13 and 14 on a small server

This has results for sysbench with PostgreSQL versions 12.4, 13.4 and 14.0 using the same setup as previously used for the insert benchmark.

Executive summary:

  • Postgres continues to be boring. There are a few regressions and more improvements.
  • There were ~40 tests and each ran for 5 minutes. Running each test for such a short time means there is more chance for confusing results. Alas, running each test for 30 minutes or more would have taken too long because I repeated the benchmark many times for different DBMS, configurations and database sizes. I started another round just for the Postgres IO-bound configuration that will take about 3 days to get results.

Details

See the insert benchmark post for more details on the hardware and Postgres configurations. As always, there are layers upon layers of shell scripts. The file all_small.sh lists the sequence in which the test steps are run and each step is run for 300 seconds with 1 connection (low concurrency!). From the all_small.sh script there were 42 different invocations of sysbench, each one is a microbenchmark. There should have been 43 but a typo got in the way. From 42 X 5 minutes for the tests plus more time for the load it takes 4 to 8 hours for all_small.sh to get results for each DBMS + configuration.

Three configurations were tested: 10M rows without prepared statements, 10M rows with prepared statements, 400M rows without prepared statements. The 10M row tests are CPU-bound as the database fits in memory. The 400M row test is usually IO-bound as the database is larger than memory. Below I call the test configurations 10m.prep0, 10m.prep1 and 400m.prep0. The database sizes are 2.6G at 10M rows and 99G at 400M rows after the initial load.

All of the shell scripts are here and the tests were run using a script like this.

I use my fork of sysbench that includes a few more tests (Lua scripts). The Lua scripts are here. My fork might be a few years behind upstream.

How to read the results

The presentation for these results is just text files pasted into gists. There are two types of files: qps and metrics. 

The qps file has the relative QPS for each test where the relative QPS is the ratio: QPS-for-me / QPS-for-base.

  • QPS-for-me is the QPS for one of Postgres 12.4, 13.4 or 14.0
  • QPS-for-base is the QPS for Postgres 12.4.
  • Thus each line in the qps file has three numeric columns and the first one has the value 1.00 (QPS- for-12.4 / QPS-for-12.4). The value in the second column is the QPS for 13.4 relative to 12.4, and in the third is the QPS for 14.0 relative to 12.4. When the value in the second or third column is less than 1.0 then that version is slower than 12.4.
The metrics file has absolute and relative (to results for 12.4) values for each of the 42 test steps. This file is long so I start with the qps file and then consult the metrics file to understand why one result is better than another.
  • cpu/o - CPU per operation, measured by iostat. While there is a unit for this, I don't worry about that as it is most useful for comparing numbers between different test configurations so the units drop out.
  • r/o - storage reads per operation, measured by iostat
  • rKB/o - KB read from storage per operation, measured by iostat
  • wKB/o - KB written to storage per operation,  measured by iostat
  • o/s - operations/second (QPS, inserts/s, etc). I tend to use QPS below for everything.
  • dbms - the Postgres version and configuration file
Results

Here are the qps and metrics files for the 10m.prep0 (CPU-bound, no prepared statements):
  • scan.range.pk1 has a regression in 13.4 that was mostly fixed in 14.0. The QPS relative to 12.4 is 0.82 for 13.4 and 0.96 for 14.0. From the metrics file there is a 30% increase in CPU overhead (see the cpu/o column) in 13.4. The scan test does a full scan of the test table using a WHERE clause that filters all rows: SELECT * from %s WHERE LENGTH(c) < 0.

Here are the qps and metrics files for the 10m.prep1 (CPU-bound, prepared statements):
  • Results are similar to 10m.prep0, the only regression is for scan.range.pk1 for 13.4.

Here are the qps and metrics files for the 400m.prep0 (IO-bound, no prepared statements):
  • scan.range.pk1 has a regression from CPU overhead for 13.4
  • read-write.range10.pk has a ~10% regression in 13.4 and 14.0. The biggest difference from the metrics file is for wKB/o. Perhaps this test needs to run for more time to get a better signal.
  • Five of the tests improve by ~10% or more in 13.4 and 14.0. I don't count the improvement for random-points.pre.range1000.pk1 because the QPS is too small (5 & 6) and rounding might explain the difference. Reduced CPU/query doesn't explain all of the improvements as in many cases there is also less IO/query but my Postgres expertise isn't strong enough to have a good guess. There have been improvements to vacuum and b-tree indexes that are likely part of the reason.
Updates

I repeated the IO-bound tests with more time per microbenchmark, 1800 seconds rather than 300. Here are the qps and metrics files. For 14.0, there were 3 tests for which perf improved more than ~5% and 5 for which it got worse by more than ~5%.

This lists microbenchmarks for which the QPS ratio is > 1.05 for Postgres 13.4 or 14.0 relative to 12.4. I picked 1.05 as a cutoff. There aren't any huge speedups, but it is nice to see some improvements.

12.4    13.4    14.0
1.00    1.12    1.13    point-query.warm.range100.pk1
1.00    1.04    1.07    points-notcovered-si.pre.range100.pk1
1.00    1.02    1.07    read-only.range10.pk1
1.00    1.05    1.07    points-notcovered-si.range100.pk1

And this lists microbenchmarks for which the QPS ratio is < =.95 for Postgres 13.4 or 14.0 relative to 12.4. From the metrics file, new CPU overhead isn't the root cause for 14.0.

12.4    13.4    14.0
1.00    0.98    0.94    update-inlist.range100.pk1
1.00    0.84    0.93    scan.range100.pk1
1.00    1.04    0.93    insert.range100.pk1