53c8c43d8a
CQL supports type casting using C-style casts. For example it's possible to do: `blob_column = (blob)funcReturningInt()` This functionality is pretty limited, we only allow such casts between types that have a compatible binary representation. Compatible means that the bytes will stay unchanged after the conversion. This means that it's legal to cast an int to blob (int is just a 4 byte blob), but it's illegal to cast a bigint to int (change 4 bytes -> 8 bytes). This simplifies things, to cast we can just reinterpret the value as the other type. Another use of C-style casts are type hints. Sometimes it's impossible to infer the exact type of an expression from the context. In such cases the type can be specified by casting the expression to this type. For example: `overloadedFunction((int)?)` Without the cast it would be impossible to guess what should be the bind marker's type. The function is overloaded, so there are many possible argument types. The type hint specifies that the bind marker has type int. An interesting thing is that such casts don't have to be explicit. CQL allows to put an int value in a place where a blob value is expected and it will be automatically converted without any explicit casting. --- I started looking at our implementation of casts because of #12900. In there the author expressed the need to specify a type hint for bind marker used to pass the WASM code. It could be either `(text)?` for text WASM, or `(blob)?` for binary WASM. This specific use of type hints wasn't supported because there was no `receiver` and the implementation of `prepare_expression` didn't handle that. Preparing casts without a receiver should be easy to implement - we can infer the type of the expression by looking at the type to which the expression is cast. But while reading `prepare_expression` for `expr::cast` I noticed that the code there is a bit strange. The implementation prepared the expression to cast using the original `receiver` instead of a receiver with the cast type. This caused some issues because of which casting didn't work as expected. For example it was possible to do: ```cql blob_column = (blob)funcReturningInt() ``` But this didn't work at all: ```cql blob_column = (blob)(int)12323 ``` It tried to prepare `untyped_contant(12323)` with a `blob` receiver, which fails. This makes `expr::cast` useless for casting. Casting when the representation is compatible is already implicit. I couldn't find a single case where adding a cast would change the behavior in any way. There was some use for it as a type hint to choose a specific overload of a function, but it was worthless for casting. Cassandra has the same issue, I created a `cql-pytest` test and it showed that we behave in the same way as Cassandra does. I decided to improve this. By preparing the expression using a receiver with the cast type, `expr::cast` becomes actually useful for casting values. Things like `(blob)(int)12323` now work without any issues. This diverges from the behavior in Cassandra, but it's an extension, not a breaking incompatibility. --- This PR improves `prepare_expression` for `expr::cast` in the following ways: 1) Support for more complex casts by preparing the expression using a different receiver. This makes casts like `(blob)(int)123` possible 2) Support preparing `expr::cast` without a receiver. Type inference chooses the cast type as the type of the expression. 3) Add pytest tests for C-style casts `2)` Is needed for #12900, the other changes is just something I decided to do since I was already working on this piece of code. Closes #13053 * github.com:scylladb/scylladb: expr_test: more tests for preparing bind variables with type hints prepare_expr: implement preparing expr::cast with no receiver prepare_expr: use :user formatting in cast_prepare_expression prepare_expr: remove std::get<> in cast_prepare_expression prepare_expr: improve cast_prepare_expression prepare_expr: improve readability in cast_prepare_expression cql-pytest: test expr::cast in test_cast.py
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.