[ASTERIXDB-2771][*DB] Enabling LSM filters on datasets with meta records

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

Details:
This patch enables LSM filters on datasets with meta records. It
introduces a filterSourceIndicator that indicates where the filter value
comes from, (null - no filter, 0 - from the record, 1 - from the meta
record). In LangExpressionToPlanTranslator, filter and meta handling
are pushed into the translate(Insert/Upsert/Delete) methods separately.
Currently, only UPSERTs are allowed on datasets with meta records, and
only in this case, the filter value may come from the meta records.
This patch also renamed an existing test case with where on change feed
to avoid confuison. Legacy datasets without filterSourceIndicator will
have 0 (pointing to record) by default.

Change-Id: I6189169cafab9d99b8662ec91cbdd801cfae9dba
Reviewed-on: https://asterix-gerrit.ics.uci.edu/c/asterixdb/+/7647
Integration-Tests: Jenkins <jenkins@fulliautomatix.ics.uci.edu>
Tested-by: Jenkins <jenkins@fulliautomatix.ics.uci.edu>
Reviewed-by: Ali Alsuliman <ali.al.solaiman@gmail.com>
80 files changed
tree: 70d2fa0444f44161550c0d88226c22ba52d6be12
  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
    Two expressive and declarative query languages (SQL++ and AQL) that support 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

Community support