| commit | 6d6b2904ff6eb3f0a3f6f642aafb99ef101e7b47 | [log] [tgz] |
|---|---|---|
| author | Murtadha Hubail <mhubail@apache.org> | Thu Jun 20 13:52:20 2019 +0300 |
| committer | Murtadha Hubail <mhubail@apache.org> | Mon Jun 24 20:27:14 2019 +0000 |
| tree | 01077ce5e0aee8b7f9eba50d483a287af2b186c7 | |
| parent | f79924f2d59d5ae93bd9ed9be39d93df4b8c92d1 [diff] |
[ASTERIXDB-2598][RT] Add Support For Runtime Warnings - user model changes: yes - storage format changes: no - interface changes: yes Details: - Add the ability to add runtime warnings per task and return the generated warnings as part of the task profile on task completion. - On successful job completion, aggregate warnings from all task profiles of a job. - Return the generated warnings in the query service response as an array of "warnings" each with a code, which is currently hard-coded to 1, and a message. - Fix propagating source location to scalar aggregate functions. - Add a flag in test cases definition to indicate whether or not to check for expected warnings and default it to false. - Generate warnings when min/max functions encounter incomparable type or unsupported input. - Add support in test framework to extract warnings along with extracting the result field. - Add support in test framework to validate generated and expected warnings. - Add test cases for min/max generated warnings. Change-Id: I52fa5b807799487d62e67a8861068e1547aa629a Reviewed-on: https://asterix-gerrit.ics.uci.edu/3451 Integration-Tests: Jenkins <jenkins@fulliautomatix.ics.uci.edu> Reviewed-by: Murtadha Hubail <mhubail@apache.org> Tested-by: Jenkins <jenkins@fulliautomatix.ics.uci.edu> Reviewed-by: Dmitry Lychagin <dmitry.lychagin@couchbase.com>
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.
To build AsterixDB from source, you should have a platform with the following:
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
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:19001
Read more documentation to learn the data model, query language, and how to create a cluster instance.