Quasar is a purely-functional compiler and optimizing planner for queries expressed in terms of the Multidimensional Relational Algebra (MRA). Quasar has support for arbitrary backends, both heavyweight (full evaluation engines) and lightweight (simple reads with optional pushdown of structural operations and columnar predicates), including full classpath isolation for lightweight backends.
It's important to note that Quasar is not, in and of itself, a runnable application. It is a library which is used by the broader Precog product, much of which is closed-source. Contributions are very much welcome, as is feedback, questions, and general conversation. Join the Discord!
Building and Testing
Quasar builds with SBT:
If running on Windows, you may use the SBT batch file instead of the shell script.
Probably the most interesting part of the codebase is the optimizing query planner, which is implemented in the qsu submodule, based on data structures defined in qscript. I recommend starting by looking at the
LPtoQS class, which defines a kleisli composition that clearly lays out all of the phases of the compiler. The core data structure used by the compiler is
QSUGraph, which is a purely functional representation of a directed acyclic graph, which in turn represents data flow in a query.
The formulation of the query plan itself is a fixed-point data structure dictated by several pattern functors composed via coproducts. The primary such pattern functor is
QScriptCore. You would generally deconstruct and interpret this query plan using general folds provided by matryoshka.
Query operations which are pushed down to the underlying data source are represented by
ScalarStage, and carried via
InterpretedRead. Data sources are always free to only implement a subset of the pushdown functionality.
The codebase makes extremely heavy use of Scalaz and Cats throughout (using shims to solve the impedance between them), and many high-level operations (such as datasets) are represented as fs2 streams.
Datasource implementation providing access to the filesystems local to the JVM.
Configuration for the local datasource has the following JSON format
"variant": "array-wrapped" | "line-delimited",
rootDiran absolute path to a local directory at which to root the datasource, all paths handled by the datasource will be interpreted relative to this physical directory.
formatthe format of all resources in the datasource, currently JSON is supported in both array-wrapped and line-delimited variants.
readChunkSizeBytes(optional) an integer indicating the chunk size to use when reading local files, the default is
1048576(1MB). Different values may yield higher throughput depending on the filesystem.
compressionScheme(optional) whether to expect resources to be compressed, currently
gzipis the only supported compression scheme. Omitting this option indicates uncompressed resources.
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