Sunday, April 22, 2018

MyRocks, malloc and fragmentation -- a strong case for jemalloc

While trying to reproduce a MyRocks performance problem I ran a test using a 4gb block cache and tried both jemalloc and glibc malloc. The test server uses Ubuntu 16.04 which has glibc 2.23 today. The table below lists the VSZ and RSS values for the mysqld process after a test table has been loaded. RSS with glibc malloc is 2.6x larger than with jemalloc. MyRocks and RocksDB are much harder on an allocator than InnoDB and this test shows the value of jemalloc.

VSZ(gb) RSS(gb) malloc  
 7.9     4.8    jemalloc-3.6.0
13.6    12.4    glibc-2.23

I am not sure that it is possible to use a large RocksDB block cache with glibc malloc, where large means that it gets about 80% of RAM.

I previously shared results for MySQL and for MongoDB. There have been improvements over the past few years to make glibc malloc perform better on many-core servers. I don't know whether that work also made it better at avoiding fragmentation.

Friday, April 20, 2018

Fun with caching_sha2_password in MySQL 8.0.11


I want to get benchmark numbers with MySQL 8.0.11. This is my first impression. The default auth method was changed to caching_sha2_password. See this post for more details. There will be some confusion with this change. By confusion I mean the difference between "error" and "OK because cached" below. I am not alone. See the experience that an expert had with replication.

Fun with caching_sha2_password occurs even with clients compiled as part of 8.0.11:

  1. install MySQL 8.0.11, disable SSL but use mostly default my.cnf
  2. bin/mysql -u... -p... -h127.0.0.1 -e ... -> error
  3. bin/mysql -u... -p... -e ... -> OK
  4. bin/mysql -u... -p... -h127.0.0.1 -e ... -> OK because cached

The error in step 2 is: ERROR 2061 (HY000): Authentication plugin 'caching_sha2_password' reported error: Authentication requires secure connection.

From show global variables I see the default is caching_sha2_password:

default_authentication_plugin   caching_sha2_password
Setting this in my.cnf after I created the user doesn't fix the problem. Setting this before creating the user is one fix. I did not test whether changing the value of user.plugin to "mysql_native_password" is another workaround.
default_authentication_plugin=mysql_native_password
The error when using an old mysql client will also be a source of confusion:
$ ~/b/orig5635/bin/mysql -u... -p.. -h127.0.0.1
ERROR 2059 (HY000): Authentication plugin 'caching_sha2_password' cannot be loaded: /home/mdcallag/b/orig5635/lib/plugin/caching_sha2_password.so: cannot open shared object file: No such file or directory
 

Thursday, April 5, 2018

Index Structures, Access Methods, whatever

I prefer to use Index Structures when writing about algorithms for indexing data stored on disk and SSD but Access Methods is another popular name. Recently I have been working on a high-level comparison (lots of hand waving) of index structures in terms of read, write, space and cache amplification.

I started by dividing the index structures into tree-based and hash-based. Tree-based support range queries while hash-based do not. Most of my experience is with tree-based approaches. There was a lot of research on hash-based in the 80s (extendible, dynamic and linear hashing), they are in production today but not as prominent as b-trees, and there is recent research on them. This paper by Seltzer and Yigit is a great overview on hash-based index structures.

The next classification is by algorithm - update-in-place, index+log and LSM. I use this for both tree-based and hash-based so LSM means Log Structured Merge rather than Log Structured Merge Tree. Most DBMS implement one or two of these. Tarantool has more algorithmic diversity, but I don't have much experience with it.

For tree-based:
  • update-in-place - an update-in-place B-Tree is the best example including clustered (InnoDB) and non-clustered (PostgreSQL). Even though it isn't update-in-place, I expect WiredTiger to be close enough given that it is copy-on-write-random (CoW-R).
  • LSM - leveled and tiered compaction are examples (RocksDB, LevelDB, Cassandra). Note that the original LSM design by O'Neil et al didn't have an L0 and I suspect that the L0 might be a mistake but that is for another post. I don't want to insult LevelDB authors, the L0 makes sense when you want to limit memory consumption for database embedded in client apps, I am not sure it makes sense for serious OLTP with RocksDB. O'Neil also did fundamental work on bitmap indexes. I worked on both so he made my career possible (thank you).
  • index+log - use a tree-based index with data in log segments. The index points into the log segments. GC is required. It scans the log segments and probes the index to determine whether a record is live or can be deleted. There are fewer examples of this approach but interesting systems include WiscKey and ForestDB. Range queries will suffer unless the index is covering (this needs more discussion, but not here).

For hash-based:
  • update-in-place - see dynamic, extendible and linear hashing. TIL that ZFS implements extendible hashing. BerkeleyDB supports linear hashing. The dbm implementation on many Linux/Unix systems also implements some variant of update-in-place persistent hash tables.
  • LSM - today I only know of one example of this - SILT. That is a great paper (read it). I include it as an example of hash-based even though one of the levels is ordered.
  • index+log - BitCask is one example but the index wasn't durable and it took a long time (scan all log segments) to rebuild it on process start. Faster might be another example, but I am still reading the paper. I hope someone names a future system Efficienter or Greener.

Finally, I have a few comments on performance. I will be brief, maybe there will be another post with a lot more detail on my opinions:
  • b-tree - provides better read efficiency at the cost of write efficiency. The worst case for write efficiency is writing back a page for every modified row. Cache efficiency is better for a clustered index than a non-clustered index -- for a clustered index you should cache at least one key/pointer per leaf block but for a non-clustered index you need the entire index in RAM or there will be extra storage reads. Space efficiency for a b-tree is good (not great, not bad) -- the problem is fragmentation.
  • LSM - provides better write efficiency at the cost of read efficiency. Leveled compaction provides amazing space efficiency. Tiered compaction gets better write efficiency at the cost of space efficiency. Compaction does many key comparisons and this should be considered as part of the CPU overhead for an insert (maybe 4X more comparisons/insert than a b-tree).
  • index+log - provides better write efficiency. Depending on the choice of index structure this doesn't sacrifice read efficiency like an LSM. But the entire index must remain in RAM (just like a non-clustered b-tree) or GC will fall behind and/or do too many storage reads. GC does many index probes and the cost of this is greatly reduced by using a hash-based solution. These comparisons should be considered as part of the CPU overhead of an insert. There is also a write vs space efficiency tradeoff. By increasing the amount of dead data that can be tolerated in log segments then GC is less frequent, write-amplification is improved but space-amplification suffers. There are variants that don't need GC, but they are not general purpose.