80989556ac
This patch adds a set of alternator workloads to pgo training script. To confirm that added workloads are indeed affecting profile we can compare: ⤖ llvm-profdata show ./build/release-pgo/profiles/workdirs/clustering/prof.profdata Instrumentation level: IR entry_first = 0 Total functions: 105075 Maximum function count: 1079870885 Maximum internal block count: 2197851358 and ⤖ llvm-profdata show ./build/release-pgo/profiles/workdirs/alternator/prof.profdata Instrumentation level: IR entry_first = 0 Total functions: 105075 Maximum function count: 5240506052 Maximum internal block count: 9112894084 to see that function counters are on similar levels, they are around 5x higher for alternator but that's because it combines 5 specific sub-workloads. To confirm that final profile contains alterantor functions we can inspect: ⤖ llvm-profdata show --counts --function=alternator --value-cutoff 100000 ./build/release-pgo/profiles/merged.profdata (...) Instrumentation level: IR entry_first = 0 Functions shown: 356 Total functions: 105075 Number of functions with maximum count (< 100000): 97275 Number of functions with maximum count (>= 100000): 7800 Maximum function count: 7248370728 Maximum internal block count: 13722347326 we can see that 356 functions which symbol name contains word alternator were identified as 'hot' (with max count grater than 100'000). Running: ⤖ llvm-profdata show --counts --function=alternator --value-cutoff 1 ./build/release-pgo/profiles/merged.profdata (...) Instrumentation level: IR entry_first = 0 Functions shown: 806 Total functions: 105075 Number of functions with maximum count (< 1): 67036 Number of functions with maximum count (>= 1): 38039 Maximum function count: 7248370728 Maximum internal block count: 13722347326 we can see that 806 alternator functions were executed at least once during training. And finally to confirm that alternator specific PGO brings any speedups we run: for workload in read scan write write_gsi write_rmw do ./build/release/scylla perf-alternator-workloads --smp 4 --cpuset "10,12,14,16" --workload $workload --duration 1 --remote-host 127.0.0.1 2> /dev/null | grep median done results BEFORE: median 258137.51910849303 median absolute deviation: 786.06 median 547.2578202937141 median absolute deviation: 6.33 median 145718.19856685458 median absolute deviation: 5689.79 median 89024.67095807113 median absolute deviation: 1302.56 median 43708.101729598646 median absolute deviation: 294.47 results AFTER: median 303968.55333940056 median absolute deviation: 1152.19 median 622.4757636209254 median absolute deviation: 8.42 median 198566.0403745328 median absolute deviation: 1689.96 median 91696.44912842038 median absolute deviation: 1891.84 median 51445.356525664996 median absolute deviation: 1780.15 We can see that single node cluster tps increase is typically 13% - 17% with notable exceptions, improvement for write_gsi is 3% and for write workload whopping 36%. The increase is on top of CQL PGO. Write workload is executed more often because it's involved also as data preparation for read and scan. Some further improvement could be to separate preparation from training as it's done for CQL but it would be a bit odd if ~3x higher counters for one flow have so big impact. Additional disclaimers: - tests are performing exactly the same workloads as in training so there might be some bias - tests are running single node cluster, more realistic setup will likely show lower improvement Fixes https://github.com/scylladb/scylla-enterprise/issues/4066
Scylla
What is Scylla?
Scylla is the real-time big data database that is API-compatible with Apache Cassandra and Amazon DynamoDB. Scylla embraces a shared-nothing approach that increases throughput and storage capacity to realize order-of-magnitude performance improvements and reduce hardware costs.
For more information, please see the ScyllaDB web site.
Build Prerequisites
Scylla is fairly fussy about its build environment, requiring very recent versions of the C++23 compiler and of many libraries to build. The document HACKING.md includes detailed information on building and developing Scylla, but to get Scylla building quickly on (almost) any build machine, Scylla offers a frozen toolchain, This is a pre-configured Docker image which includes recent versions of all the required compilers, libraries and build tools. Using the frozen toolchain allows you to avoid changing anything in your build machine to meet Scylla's requirements - you just need to meet the frozen toolchain's prerequisites (mostly, Docker or Podman being available).
Building Scylla
Building Scylla with the frozen toolchain dbuild is as easy as:
$ git submodule update --init --force --recursive
$ ./tools/toolchain/dbuild ./configure.py
$ ./tools/toolchain/dbuild ninja build/release/scylla
For further information, please see:
- Developer documentation for more information on building Scylla.
- Build documentation on how to build Scylla binaries, tests, and packages.
- Docker image build documentation for information on how to build Docker images.
Running Scylla
To start Scylla server, run:
$ ./tools/toolchain/dbuild ./build/release/scylla --workdir tmp --smp 1 --developer-mode 1
This will start a Scylla node with one CPU core allocated to it and data files stored in the tmp directory.
The --developer-mode is needed to disable the various checks Scylla performs at startup to ensure the machine is configured for maximum performance (not relevant on development workstations).
Please note that you need to run Scylla with dbuild if you built it with the frozen toolchain.
For more run options, run:
$ ./tools/toolchain/dbuild ./build/release/scylla --help
Testing
See test.py manual.
Scylla APIs and compatibility
By default, Scylla is compatible with Apache Cassandra and its API - CQL. There is also support for the API of Amazon DynamoDB™, which needs to be enabled and configured in order to be used. For more information on how to enable the DynamoDB™ API in Scylla, and the current compatibility of this feature as well as Scylla-specific extensions, see Alternator and Getting started with Alternator.
Documentation
Documentation can be found here. Seastar documentation can be found here. User documentation can be found here.
Training
Training material and online courses can be found at Scylla University. The courses are free, self-paced and include hands-on examples. They cover a variety of topics including Scylla data modeling, administration, architecture, basic NoSQL concepts, using drivers for application development, Scylla setup, failover, compactions, multi-datacenters and how Scylla integrates with third-party applications.
Contributing to Scylla
If you want to report a bug or submit a pull request or a patch, please read the contribution guidelines.
If you are a developer working on Scylla, please read the developer guidelines.
Contact
- The community forum and Slack channel are for users to discuss configuration, management, and operations of the ScyllaDB open source.
- The developers mailing list is for developers and people interested in following the development of ScyllaDB to discuss technical topics.