[ASTERIXDB-3576][EXT] push predicates down to delta tables to filter row groups

- user model changes: no
- storage format changes: no
- interface changes: no

Details:
Delta table's data files are essentially Parquet files. Parquet allows
applying a predicate while reading data files to skip row groups.
With this patch we pushdown filters to individual parquet files of the
Delta table to filter row groups. The Predicate class of the Delta Kernel API
is not serializable, so we have added a custom serialization/de-serialization
of Delta kernel APIs Predicates.

Ext-ref: MB-65315
Change-Id: I9fa1a84d7be63ada7b9768a81984b2172e7401b3
Reviewed-on: https://asterix-gerrit.ics.uci.edu/c/asterixdb/+/19527
Integration-Tests: Jenkins <jenkins@fulliautomatix.ics.uci.edu>
Reviewed-by: Peeyush Gupta <peeyush.gupta@couchbase.com>
Reviewed-by: Ali Alsuliman <ali.al.solaiman@gmail.com>
Tested-by: Jenkins <jenkins@fulliautomatix.ics.uci.edu>
3 files changed
tree: 4ecb5915f525160d7b0ea15351c94310739553cb
  1. .gitattributes
  2. .gitignore
  3. README.md
  4. asterixdb/
  5. build.xml
  6. hyracks-fullstack/
  7. pom.xml
README.md

What is AsterixDB?

AsterixDB is a BDMS (Big Data Management System) with a rich feature set that sets it apart from other Big Data platforms. Its feature set makes it well-suited to modern needs such as web data warehousing and social data storage and analysis. AsterixDB has:

  • Data model
    A semistructured NoSQL style data model (ADM) resulting from extending JSON with object database ideas

  • Query languages
    An expressive and declarative query language (SQL++ that supports a broad range of queries and analysis over semistructured data

  • Scalability
    A parallel runtime query execution engine, Apache Hyracks, that has been scale-tested on up to 1000+ cores and 500+ disks

  • Native storage
    Partitioned LSM-based data storage and indexing to support efficient ingestion and management of semistructured data

  • External storage
    Support for query access to externally stored data (e.g., data in HDFS) as well as to data stored natively by AsterixDB

  • Data types
    A rich set of primitive data types, including spatial and temporal data in addition to integer, floating point, and textual data

  • Indexing
    Secondary indexing options that include B+ trees, R trees, and inverted keyword (exact and fuzzy) index types

  • Transactions
    Basic transactional (concurrency and recovery) capabilities akin to those of a NoSQL store

Learn more about AsterixDB at its website.

Build from source

To build AsterixDB from source, you should have a platform with the following:

  • A Unix-ish environment (Linux, OS X, will all do).
  • git
  • Maven 3.3.9 or newer.
  • JDK 11 or newer.
  • Python 3.6+ with pip and venv

Instructions for building the master:

  • Checkout AsterixDB master:

      $git clone https://github.com/apache/asterixdb.git
    
  • Build AsterixDB master:

      $cd asterixdb
      $mvn clean package -DskipTests
    

Run the build on your machine

Here are steps to get AsterixDB running on your local machine:

  • Start a single-machine AsterixDB instance:

      $cd asterixdb/asterix-server/target/asterix-server-*-binary-assembly/apache-asterixdb-*-SNAPSHOT
      $./opt/local/bin/start-sample-cluster.sh
    
  • Good to go and run queries in your browser at:

      http://localhost:19006
    
  • Read more documentation to learn the data model, query language, and how to create a cluster instance.

Documentation

To generate the documentation, run asterix-doc with the generate.rr profile in maven, e.g mvn -Pgenerate.rr ... Be sure to run mvn package beforehand or run mvn site in asterix-lang-sqlpp to generate some resources that are used in the documentation that are generated directly from the grammar.

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