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db31542805
This is relevant only when using partition or clustering keys which
have a representation in memory which is larger than 12.8 KB (10% of
LSA segment size).
There are several places in code (cache, background garbage
collection) which may need to linearize keys because of performing key
comparison, but it's not done safely:
1) the code does not run with the LSA region locked, so pointers may
get invalidated on linearization if it needs to reclaim memory. This
is fixed by running the code inside an allocating section.
2) LSA region is locked, but the scope of
with_linearized_managed_bytes() encloses the allocating section. If
allocating section needs to reclaim, linearization context will
contain invalidated pointers. The fix is to reorder the scopes so
that linearization context lives within an allocating section.
Example of 1 can be found in
range_populating_reader::handle_end_of_stream() where it performs a
lookup:
auto prev = std::prev(it);
if (prev->key().equal(*_cache._schema, *_last_key->_key)) {
it->set_continuous(true);
but handle_end_of_stream() is not invoked under allocating section.
Example of 2 can be found in mutation_cleaner_impl::merge_some() where
it does:
return with_linearized_managed_bytes([&] {
...
return _worker_state->alloc_section(region, [&] {
Fixes #6637.
Refs #6108.
Tests:
- unit (all)
Message-Id: <1592218544-9435-1-git-send-email-tgrabiec@scylladb.com>
(cherry picked from commit e81fc1f095)
Scylla
Quick-start
To get the build going quickly, Scylla offers a frozen toolchain which would build and run Scylla using a pre-configured Docker image. Using the frozen toolchain will also isolate all of the installed dependencies in a Docker container. Assuming you have met the toolchain prerequisites, which is running Docker in user mode, building and running is as easy as:
$ ./tools/toolchain/dbuild ./configure.py
$ ./tools/toolchain/dbuild ninja build/release/scylla
$ ./tools/toolchain/dbuild ./build/release/scylla --developer-mode 1
Please see HACKING.md for detailed information on building and developing Scylla.
Note: GCC >= 8.1.1 is required to compile Scylla.
Running Scylla
- Run Scylla
./build/release/scylla
- run Scylla with one CPU and ./tmp as work directory
./build/release/scylla --workdir tmp --smp 1
- For more run options:
./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 experimental support for the API of Amazon DynamoDB, but being experimental it needs to be explicitly enabled to be used. For more information on how to enable the experimental DynamoDB compatibility in Scylla, and the current limitations of this feature, see Alternator and Getting started with Alternator.
Documentation
Documentation can be found in ./docs and on the wiki. There is currently no clear definition of what goes where, so when looking for something be sure to check both. 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.
Building Fedora-based Docker image
Build a Docker image with:
cd dist/docker
docker build -t <image-name> .
Run the image with:
docker run -p $(hostname -i):9042:9042 -i -t <image name>