300e549267
A write to a base table can generate one or more writes to a materialized
view. The write to RF base replicas need to cause writes to RF view
replicas. Our MV implementation, based on Cassandra's implementation,
does this via "pairing": Each one of the base replicas involved in this
write sends each view update to exactly one view replica. The function
get_view_natural_endpoint() tells a base replica which of the view
replicas it should send the update to.
The standard pairing is based on the ring order: The first owner of the
base token sends to the first owner of the view token, the second to the
second, and so on. However, the existing code also uses an optimization
we call self-pairing: If a single node is both a base replica and a base
replica, the pairing is modified so this node sends the update to itself.
This patch *disables* the self-pairing optimization in keyspaces that
use tablets:
The self-pairing optimization can cause the pairing to change after
token ranges are moved between nodes, so it can break base-view consistency
in some edge cases, leading to "ghost rows". With tablets, these range
movements become even more frequent - they can happen even if the
cluster doesn't grow. This is why we want to solve this problem for tablets.
For backward compatibility and to avoid sudden inconsistencies emerging
during upgrades, we decided to continue using the self-pairing optimization
for keyspaces that are *not* using tablets (i.e., using vnoodes).
Currently, we don't introduce a "CREATE MATERIALIZED VIEW" option to
override these defaults - i.e., we don't provide a way to disable
self-pairing with vnodes or to enable them with tablets. We could introduce
such a schema flag later, if we ever want to (and I'm not sure we want to).
It's important to note, that in some cases, this change has implications
on when view updates become synchronous, in the tablets case.
For example:
* If we have 3 nodes and RF=3, with the self-pairing optimization each
node is paired with itself, the view update is local, and is
implicitly synchronous (without requiring a "synchronous_updates"
flag).
* In the same setup with tablets, without the self-pairing optimization
(due to this patch), this is not guaranteed. Some view updates may not
be synchronous, i.e., the base write will not wait for the view
write. If the user really wants synchronous updates, they should
be requested explicitly, with the "synchronous_updates" view option.
Fixes #16260.
Signed-off-by: Nadav Har'El <nyh@scylladb.com>
Closes scylladb/scylladb#16272
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.