| commit | c71173d12335a41e6a9b8e4ca8b8d435bad22b5e | [log] [tgz] |
|---|---|---|
| author | Hussain Towaileb <Hussain.Towaileb@Couchbase.com> | Thu Jun 20 05:26:43 2019 +0300 |
| committer | Hussain Towaileb <hussainht@gmail.com> | Fri Jun 28 09:59:18 2019 +0000 |
| tree | a1f3ecedfdea83e4b25affa16bc81f04774e19fb | |
| parent | 60ab58df1515832f6d0b741dae05d827cc54bd4a [diff] |
[ASTERIXDB-2593][FUN] TPC-DS always parallelize + gen all tables
- user model changes: yes
- user can call the tpcds_datagen() by passing only the scaling
factor, and that will automatically generate all the tables.
- storage format changes: no
- interface changes: no
Details:
- Added support to a second version of the tpcds_datagen()
function to take only a single parameter that will result in
generating the data for all the tables in one go. The user
now can generate the data for a single table at a time, or
all the tables in one go.
- Overridden the behavior for activating the parallelism.
Before, the library wouldn't activate parallelism unless
the tables are big, and the number of data generated per
table is over 1,000,000. With this change, the parallelism
is always activated, regardless of the table size or the
data size being generated.
- Added a new test, TPCExecutionTest, to execute the long
tests for the TPC. Some of the TPC tests take longer than
5 minutes to finish and could result in high usage of
disk space.
Change-Id: Iff199b0c533d22bcae1caf5057788b257ba4e486
Reviewed-on: https://asterix-gerrit.ics.uci.edu/3437
Sonar-Qube: Jenkins <jenkins@fulliautomatix.ics.uci.edu>
Tested-by: Jenkins <jenkins@fulliautomatix.ics.uci.edu>
Integration-Tests: Jenkins <jenkins@fulliautomatix.ics.uci.edu>
Reviewed-by: Murtadha Hubail <mhubail@apache.org>
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.