c8397f0287
The motivation for tablet resizing is that we want to keep the average tablet size reasonable, such that load rebalancing can remain efficient. Too large tablet makes migration inefficient, therefore slowing down the balancer.
If the avg size grows beyond the upper bound (split threshold), then balancer decides to split. Split spans all tablets of a table, due to power-of-two constraint.
Likewise, if the avg size decreases below the lower bound (merge threshold), then merge takes place in order to grow the avg size. Merge is not implemented yet, although this series lays foundation for it to be impĺemented later on.
A resize decision can be revoked if the avg size changes and the decision is no longer needed. For example, let's say table is being split and avg size drops below the target size (which is 50% of split threshold and 100% of merge one). That means after split, the avg size would drop below the merge threshold, causing a merge after split, which is wasteful, so it's better to just cancel the split.
Tablet metadata gains 2 new fields for managing this:
resize_type: resize decision type, can be either of "merge", "split", or "none".
resize_seq_number: a sequence number that works as the global identifier of the decision (monotonically increasing, increased by 1 on every new decision emitted by the coordinator).
A new RPC was implemented to pull stats from each table replica, such that load balancer can calculate the avg tablet size and know the "split status", for a given table. Avg size is aggregated carefully while taking RF of each DC into account (which might differ).
When a table is done splitting its storage, it loads (mirror) the resize_seq_number from tablet metadata into its local state (in another words, my split status is ready). If a table is split ready, coordinator will see that table's seq number is the same as the one in tablet metadata. Helps to distinguish stale decisions from the latest one (in case decisions are revoked and re-emited later on). Also, it's aggregated carefully, by taking the minimum among all replicas, so coordinator will only update topology when all replicas are ready.
When load balancer emits split decision, replicas will listen to need to split with a "split monitor" that is awakened once a table has replication metadata updated and detects the need for split (i.e. resize_type field is "split").
The split monitor will start splitting of compaction groups (using mechanism introduced here: 081f30d149) for the table. And once splitting work is completed, the table updates its local state as having completed split.
When coordinator pulls the split status of all replicas for a table via RPC, the balancer can see whether that table is ready for "finalizing" the decision, which is about updating tablet metadata to split each tablet into two. Once table replicas have their replication metadata updated with the new tablet count, they can update appropriately their set of compaction groups (that were previously split in the preparation step).
Fixes #16536.
Closes scylladb/scylladb#16580
* github.com:scylladb/scylladb:
test/topology_experimental_raft: Add tablet split test
replica: Bypass reshape on boot with tablets temporarily
replica: Fix table::compaction_group_for_sstable() for tablet streaming
test/topology_experimental_raft: Disable load balancer in test fencing
replica: Remap compaction groups when tablet split is finalized
service: Split tablet map when split request is finalized
replica: Update table split status if completed split compaction work
storage_service: Implement split monitor
topology_cordinator: Generate updates for resize decisions made by balancer
load_balancer: Introduce metrics for resize decisions
db: Make target tablet size a live-updateable config option
load_balancer: Implement resize decisions
service: Wire table_resize_plan into migration_plan
service: Introduce table_resize_plan
tablet_mutation_builder: Add set_resize_decision()
topology_coordinator: Wire load stats into load balancer
storage_service: Allow tablet split and migration to happen concurrently
topology_coordinator: Periodically retrieve table_load_stats
locator: Introduce topology::get_datacenter_nodes()
storage_service: Implement table_load_stats RPC
replica: Expose table_load_stats in table
replica: Introduce storage_group::live_disk_space_used()
locator: Introduce table_load_stats
tablets: Add resize decision metadata to tablet metadata
locator: Introduce resize_decision
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++20 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 APIs - CQL and Thrift. 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.