9b9d5cee8a
When we create a materialized view, we consider 2 cases: 1. the view's primary key contains a column that is not in the primary key of the base table 2. the view's primary key doesn't contain such a column In the 2nd case, we add all columns from the base table to the schema of the view (as virtual columns). As a result, all of these columns are effectively "selected" in view_updates::can_skip_view_updates. Same thing happens when we add new columns to the base table using ALTER. Because of this, we can never have !column_is_selected and !has_base_non_pk_columns_in_view_pk at the same time. And thus, the check (!column_is_selected && _base_info.has_base_non_pk_columns_in_view_pk) is always the same as (!column_is_selected). Because we immediately return after this check, the tail of this function is also never reached - all checks after the (column_is_selected) are affected by this. Also, the condition (!column_is_selected && base_has_nonexpiring_marker) is always false at the point it is called. And this in turn makes the `base_has_nonexpiring_marker` unused, so we delete it as well. It's worth considering, why did we even have `base_has_nonexpiring_marker` if it's effectively unused. We initially introduced it inbd52e05ae2and we (incorrectly) used it to allow skipping view updates even if the liveness of virtual columns changed. Soon after, in5f85a7a821, we started categorizing virtual columns as column_is_selected == true and we moved the liveness checks for virtual columns to the `if (column_is_selected)` clause, before the `base_has_nonexpiring_marker` check. We changed this because even if we have a nonexpiring marker right now, it may be changed in the future, in which case the liveness of the view row will depend on liveness of the virtual columns and we'll need to have the view updates from the time the row marker was nonexpiring. (cherry picked from commit ca1c8ff2095c6468e79990f2ad6f815e3f21aabc)
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 ScyllaDB.
- The developers mailing list is for developers and people interested in following the development of ScyllaDB to discuss technical topics.