1cefb662cd
Let's remove `expr::token` and replace all of its functionality with `expr::function_call`. `expr::token` is a struct whose job is to represent a partition key token. The idea is that when the user types in `token(p1, p2) < 1234`, this will be internally represented as an expression which uses `expr::token` to represent the `token(p1, p2)` part. The situation with `expr::token` is a bit complicated. On one hand side it's supposed to represent the partition token, but sometimes it's also assumed that it can represent a generic call to the `token()` function, for example `token(1, 2, 3)` could be a `function_call`, but it could also be `expr::token`. The query planning code assumes that each occurence of expr::token represents the partition token without checking the arguments. Because of this allowing `token(1, 2, 3)` to be represented as `expr::token` is dangerous - the query planning might think that it is `token(p1, p2, p3)` and plan the query based on this, which would be wrong. Currently `expr::token` is created only in one specific case. When the parser detects that the user typed in a restriction which has a call to `token` on the LHS it generates `expr::token`. In all other cases it generates an `expr::function_call`. Even when the `function_call` represents a valid partition token, it stays a `function_call`. During preparation there is no check to see if a `function_call` to `token` could be turned into `expr::token`. This is a bit inconsistent - sometimes `token(p1, p2, p3)` is represented as `expr::token` and the query planner handles that, but sometimes it might be represented as `function_call`, which the query planner doesn't handle. There is also a problem because there's a lot of code duplication between a `function_call` and `expr::token`. All of the evaluation and preparation is the same for `expr::token` as it's for a `function_call` to the token function. Currently it's impossible to evaluate `expr::token` and preparation has some flaws, but implementing it would basically consist of copy-pasting the corresponding code from token `function_call`. One more aspect is multi-table queries. With `expr::token` we turn a call to the `token()` function into a struct that is schema-specific. What happens when a single expression is used to make queries to multiple tables? The schema is different, so something that is represented as `expr::token` for one schema would be represented as `function_call` in the context of a different schema. Translating expressions to different tables would require careful manipulation to convert `expr::token` to `function_call` and vice versa. This could cause trouble for index queries. Overall I think it would be best to remove `expr::token`. Although having a clear marker for the partition token is sometimes nice for query planning, in my opinion the pros are outweighted by the cons. I'm a big fan of having a single way to represent things, having two separate representations of the same thing without clear boundaries between them causes trouble. Instead of having both `expr::token` and `function_call` we can just have the `function_call` and check if it represents a partition token when needed. Refs: #12906 Refs: #12677 Closes: #12905 Closes #13480 * github.com:scylladb/scylladb: cql3: remove expr::token cql3: keep a schema in visitor for extract_clustering_prefix_restrictions cql3: keep a schema inside the visitor for extract_partition_range cql3/prepare_expr: make get_lhs_receiver handle any function_call cql3/expr: properly print token function_call expr_test: use unresolved_identifier when creating token cql3/expr: split possible_lhs_values into column and token variants cql3/expr: fix error message in possible_lhs_values cql3: expr: reimplement is_satisfied_by() in terms of evaluate() cql3/expr: add a schema argument to expr::replace_token cql3/expr: add a comment for expr::has_partition_token cql3/expr: add a schema argument to expr::has_token cql3: use statement_restrictions::has_token_restrictions() wherever possible cql3/expr: add expr::is_partition_token_for_schema cql3/expr: add expr::is_token_function cql3/expr: implement preparing function_call without a receiver cql3/functions: make column family argument optional in functions::get cql3/expr: make it possible to prepare expr::constant cql3/expr: implement test_assignment for column_value cql3/expr: implement test_assignment for expr::constant
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.