Rocksdb…The Old Way…Log Structured Merge

TRocksDB

Remi Brasga Sr. Software Engineer | Memory Storage Strategy Division Toshiba Memory America, Inc. Hi!

I’m Remi (Remington Brasga).

As a Sr. Software Engineer for the Memory Storage Strategy Division at Toshiba Memory America, Inc., I am dedicated to development and collaboration on open source software. I earned my B.S. and M.S. from University of California, Irvine.

Today, I am here fresh from having my 3rd son who has kept me very busy the past few weeks.

(thanks for letting me come here and rest!) Applications are King

• Software is eating the world

• Storage is “Defined by Software” – i.e., applications define how storage is used

• Yet, applications do not always use storage wisely For Example, RocksDB

RocksDB is a popular data storage engine …used by a wide range of database applications

ArangoDB Ceph™ Cassandra® MariaDB®

Python® MyRocks Rockset

Cassandra is a registered trademark of The Apache Software Foundation. Ceph is a trademark of Red Hat, Inc. or its subsidiaries in the United States and other countries. Python is a registered trademark of the Python Software Foundation. MariaDB is a registered trademark of MariaDB in the European Union and other regions.

All other company names, product names and service names may be trademarks of their respective companies. The Challenge of Rocks

While highly effective as a data storage engine, RocksDB has some challenges for SSDs:

Write Amplification is very large 20-30X (or more) as a result of the compaction layers rewriting the same data

This can result in impact to SSD endurance. How does RocksDB work?

Keys and Values are stored Data Key together in pairs. Level K+V0 As more data is added, these Level 1 pairs waterfall Target 1GB Level 2 through the levels. Target 10 GB Level 3 Target 100 GB Level 4 This waterfall occurs Target 1000 GB during each compaction. Let’s Demonstrate RocksDB…the Old Way…Log Structured Merge

CPU

Key & Value Key & Value Key & Value

New key value database entries

DRAM

Saved to SSD in compaction level 1

Compaction Level 1 LSM

Compaction Level 2

Compaction Level 3 RocksDB…the Old Way…Log Structured Merge

CPU

More writes to DRAM compaction level 1

Compaction Level 1 Key & Value Key & Value Key & Value Key & Value Key & Value Key & Value Key & Value LSM

Compaction Level 2

Compaction Level 3 Compaction level 1 fills up RocksDB…the Old Way…Log Structured Merge

CPU Consolidated for compaction level 2

DRAM

Compaction level 1 is full; Compaction Level 1 Key & Value Key & Value Key & Value Key & Value Key & Value Key & Value Key & Value LSM Written to RAM…

Compaction Level 2

Compaction Level 3 RocksDB…the Old Way…Log Structured Merge

CPU Consolidated data written to compaction level 2

DRAM Key & Value Key & Value Key & Value Key & Value Key & Value Key & Value Key & Value

Application-generated write amplification Compaction Level 1 LSM

Compaction Level 2

Compaction Level 3 RocksDB…the Old Way…Log Structured Merge

CPU

DRAM

Compaction level 2 filling up Application-generated write amplification

Compaction Level 1 LSM

Compaction Level 2 KeyKeyKey & & &Value Value Value KeyKeyKey & & &Value Value Value KeyKeyKey & & &Value Value Value KeyKeyKey & & &Value Value Value KeyKeyKey & & &Value Value Value KeyKeyKey & & &Value Value Value KeyKeyKey & & &Value Value Value KeyKey & & Value Value KeyKey & & Value Value KeyKey & & Value Value KeyKey & & Value Value KeyKey & & Value Value KeyKey & & Value Value KeyKey & & Value Value

Compaction Level 3 RocksDB…the Old Way…Log Structured Merge

CPU Written to DRAM, and consolidated

DRAM

Compaction level 2 fills up

Compaction Level 1 LSM

Compaction Level 2

Compaction Level 3 RocksDB…the Old Way…Log Structured Merge

CPU

DRAM

Written to compaction Application-generated Compaction Level 1 level 3 LSM write amplification

Compaction Level 2

Compaction Level 3 Result

As you can see, • This results in rewriting data “that doesn’t change” during compaction • This can have a significant impact on SSD health and endurance

The good news is… There is a better way

Toshiba Memory America has re-architected RocksDB to solve this SSD endurance challenge Inspiration

We looked at some of the academic work on Rocks

– Major inspiration came from the “wisckey” from the university of Wisconsin WiscKey: Separating Keys from Values in SSD-conscious storage

https://www.usenix.org/system/files/conference/fast16/fast16-papers-lu.pdf Coding Work

Significant code was involved to split the keys from the values

• Replaced the LSM key value pairs with the ring buffer (for a Value Log - VLog) • The VLog is a large number of files, – with each file having about the same number of values as an SST* has keys • VLogFiles are organized into FIFO structures called VLogRings

• One tricky issue - keeping track of the state of the VLog over a restart • Active recycling added to compactions to reduce instances of uncompacted files

*SST=Stored String Table Coding Work

Work involved managing the keys

• While keys mostly are cached in memory • They still need to be written into LSM layers

• Compactions still occur, – about one per second as usual but only on the keys

• Coding for compaction involved – Compacting the keys – And cleaning up the stale data in the ring buffers Performance Tuning

Tuning performance in the code was a challenge

• Some changes made performance worse (against every expectation…)

• The split keys and values approach needed new test cases

• There are a number of new option settings to enable optimizations this added improvements to – Compaction picker improved ( to reduce need of active recycling ) – Write amp and space amp measuring – Bulk load efficiency Coding Merge

Code Merge:

RocksDB continues to evolve – TRocksDB is maintaining version compatibility with each current revision of RocksDB – TRocks is fully compatible and cross compiles with/into regular RocksDB code today – Once compiled Rocks or TRocks can be enabled with option settings How does TRocksDB work?

Keys and Values Data Value Log are split Key Value is unaffected by compactions Level K Keys go into compaction 0 layers (in memory) Level 1 VV Target 1GB Level 2 Target 10 GB Values are stored Level 3 separately into Target 100 GB Level 4 a ring buffer Target 1000 GB The Improved TRocksDB Method TRocksDB…the New Way Ring Buffer, Separate Keys/Values

CPU

Key Value Key Value Key Value Key Value

Create new DRAM database entries LSM

Ring Buffer TRocksDB…the New Way Ring Buffer, Separate Keys/Values

CPU

Key Value Key Value Key Value Key Value

Lookups are DRAM lightening fast LSM with keys cached in DRAM Keys stored separately (cacheable)

Ring Buffer SSD endurance improved Write Values to Ring via lowered Buffer Write Amp Our Goal during Development

Improved(reduced) Write Amplification with the same or better performance Improved Write Amplification

Write Amplification (lower is better)

4.9 5 TRocks 1.2.5.23 Rocks FB 6.4

3 2.1

2 1 1 0.6 0.6 1 -57%

-40% -40% 0 Test 1: Test 2: Test 3: Random Bulk Load Bulk Sequential Load Random Overwrites

Performance results based on internal Toshiba Memory testing. Benchmark tests were developed by Facebook and generated in July 2018. They measure RocksDB performance when data resides on flash storage and tested using the newest release RocksDB 6.1.fb. The benchmark information and test descriptions are available at https://github.com/facebook/rocksdb/wiki/Performance-Benchmarks. Same or Better Performance

Performance Comparison (lower is better)

Seconds TRocks 1.2.5.23 Rocks FB 6.4 155,081 160,000

140,000 118,244 120,000

100,000

80,000 -24%

60,000

29,742 40,000 21,565

9,890 6,938 9,595 20,000 -28% 6,155 -11% -3% 0 Test 1: Test 2: Test 3: Test 4: Random Bulk Load Bulk Sequential Load Random Overwrites Random Key Reads

• Every database is different

• Rocks and TRocks have a wide range settings / options to optimize the database

• These settings are often confusing or “left in default”

GitHub is an exclusive trademark registered in the United States by GitHub, Inc. All other company names, product names and service names may be trademarks of their respective companies. Tuning and Optimization

• Toshiba Memory America has created a “tuning” tool Fill in the blanks

– It is on a Google spreadsheet • Available from the GitHub®

– Easy to use - just fill in the blanks

– It generates known good settings Generate “known-good” options settings

Download Test Use Enjoy !

Join the project improve and contribute to the code Developed by Toshiba Memory America (Which will be renamed KIOXIA America, Inc. as of October 1, 2019)

Open Source

Improved write amplification = SSD last longer with the same or better performance

Available today: https://github.com/ToshibaMemoryAmerica

All company names, product names and service names may be trademarks of their respective companies. © 2019 Toshiba Memory America, Inc. All rights reserved. Information, including product pricing and specifications, content of services, and contact information is current and believed to be accurate on the date of the announcement, but is subject to change without prior notice. Technical and application information contained here is subject to the most recent applicable Toshiba Memory product specifications.