Can someone spell out the differences between using the Spark SQL CLI vs. Thriftserver/Beeline to query/modify data in Hive ? The Spark SQL documentation
mentions both of them but when would you use one or the other or are they equivalent alternatives from a functional point of view ?
For clarification:
spark-sql is a program that runs a single instance of Spark and you interact with it as if it were a mysql-like shell prompt and it makes use of the spark-warehouse and those types of features
Spark with Thriftserver is an application that exposes a connection to a running instance of Spark over a JDBC connection.
https://community.hortonworks.com/questions/33715/why-do-we-need-to-setup-spark-thrift-server.html
Beeline is a query / consumer tool that one uses to consume / connect to a running JDBC hive2 table (and thus in the spark documentation, they use beeline to test that the JDBC connection is in fact working). Note: query / connector programs like SQL Workbench can be made to connect to Spark with Thriftserver if it imports the proper Hive2 JDBC drivers & jars
Related
With SparkSQL Cassandra connector can a JDBC client tool (ie DBVisualizer, Tableau, Alteryx.etc) join 2 cassandra tables with SparkSQL?
All documentation I see refers to joinWithCassandraTable (which I assume only works in scala/java code or spark-shell but not a standard SQL client)
https://github.com/datastax/spark-cassandra-connector
DSE should support this if you're using JDBC driver that is available from DataStax Academy Downloads page. You'll need to run the Spark SQL Thrift server (via dse spark-sql-thriftserver command)... If you're just starting, DSE 6 has more improvements around this part (so-called Always On SQL Service (AOSS)).
Here is the old blog post that talks about ODBC driver + Spark SQL and joins, but the same should be for JDBC drivers.
I'm looking for a client jdbc driver that supports Spark SQL.
I have been using Jupyter so far to run SQL statements on Spark (running on HDInsight) and I'd like to be able to connect using JDBC so I can use third-party SQL clients (e.g. SQuirreL, SQL Explorer, etc.) instead of the notebook interface.
I found an ODBC driver from Microsoft but this doesn't help me with java-based SQL clients. I also tried downloading the Hive jdbc driver from my cluster, but the Hive JDBC driver does not appear to support more advance SQL features that Spark does. For example, the Hive driver complains about not supporting join statements that are not equajoins, where I know that this is a supported feature of Spark because I've executed the same SQL in Jupyter successfully.
the Hive JDBC driver does not appear to support more advance SQL features that Spark does
Regardless of the support that it provides, the Spark Thrift Server is fully compatible with Hive/Beeline's JDBC connection.
Therefore, that is the JAR you need to use. I have verified this works in DBVisualizer.
The alternative solution would be to run Spark code in your Java clients (non-third party tools) directly and skip the need for the JDBC connection.
Beeline script is one of the way of connecting to HiveServer2 present in Spark/bin.
I ran simple query as below.
In output I can see Map-Reduce is being launched.
I am just trying to understand what is advantage of beeline feature in Spark as it follows traditional map-reduce execution framework?
Can we use Spark RDD feature in beeline?
Thanks in advance.
Beeline is not part of Spark.
It's just a HiveServer2 client.
You can launch the Spark shell and execute queries within the shell, but this has nothing to do with Beeline. As Beeline has nothing to do with Spark.
This is one way.If you dont want to use Mapreduce you can use TEZ as engine.Which will run in memory as more faster than MR.
SET hive.execution.engine=tez;
But you can not run spark ifrom beeline.This is a standalone application which connects to hiveserver2.
I have a Cassandra cluster with a co-located Spark cluster, and I can run the usual Spark jobs by compiling them, copying them over, and using the ./spark-submit script. I wrote a small job that accepts SQL as a command-line argument, submits it to Spark as Spark SQL, Spark runs that SQL against Cassandra and writes the output to a csv file.
Now I feel like I'm going round in circles trying to figure out if it's possible to query Cassandra via Spark SQL directly in a JDBC connection (eg from Squirrel SQL). The Spark SQL documentation says
Connect through JDBC or ODBC.
A server mode provides industry standard JDBC and ODBC connectivity for
business intelligence tools.
The Spark SQL Programming Guide says
Spark SQL can also act as a distributed query engine using its JDBC/ODBC or
command-line interface. In this mode, end-users or applications can interact
with Spark SQL directly to run SQL queries, without the need to write any
code.
So I can run the Thrift Server, and submit SQL to it. But what I can't figure out, is how do I get the Thrift Server to connect to Cassandra? Do I simply pop the Datastax Cassandra Connector on the Thrift Server classpath? How do I tell the Thrift Server the IP and Port of my Cassandra cluster? Has anyone done this already and can give me some pointers?
Configure those properties in spark-default.conf file
spark.cassandra.connection.host 192.168.1.17,192.168.1.19,192.168.1.21
# if you configured security in you cassandra cluster
spark.cassandra.auth.username smb
spark.cassandra.auth.password bigdata#123
Start your thrift server with spark-cassandra-connector dependencies and mysql-connector dependencies with some port that you will connect via JDBC or Squirrel.
sbin/start-thriftserver.sh --hiveconf hive.server2.thrift.bind.host 192.168.1.17 --hiveconf hive.server2.thrift.port 10003 --jars <shade-jar>-0.0.1.jar --driver-class-path <shade-jar>-0.0.1.jar
For getting cassandra table run Spark-SQL queries like
CREATE TEMPORARY TABLE mytable USING org.apache.spark.sql.cassandra OPTIONS (cluster 'BDI Cassandra', keyspace 'testks', table 'testtable');
why don`t you use the spark-cassandra-connector and cassandra-driver-core? Just add the dependencies, specify the host address/login in your spark context and then you can read/write to cassandra using sql.
Currently we are building a reporting platform as a data store we used Shark. Since the development of Shark is stopped so we are in the phase of evaluating Spark SQL. Based on the use cases we have we had few questions.
1) We have data from various sources( MySQL, Oracle, Cassandra, Mongo). We would like to know how can we get this data into Spark SQL? Does there exist any utility which we can use? Does this utility support continuous refresh of data (sync of new add/update/delete on data store to Spark SQL?
2) Is the a way to create multiple database in Spark SQL?
3) For Reporting UI we use Jasper, we would like to connect from Jasper to Spark SQL. When we did our initial search we got to know currently there is no support for consumer to connect Spark SQL through JDBC, but in future releases you would like the add the same. We would like to know by when Spark SQL would have a stable release which would have JDBC Support? Meanwhile we took the source code from https://github.com/amplab/shark/tree/sparkSql but we had some difficulty in setting it up locally and evaluating it . It would be great if you can help us with setup instructions.(I can share the issue we are facing please let me know where can I post the error logs)
4) We would also require a SQL prompt where we can execute queries, currently Spark Shell provides SCALA prompt where SCALA code can be executed, from SCALA code we can fire SQL queries. Like Shark we would like to have SQL prompt in Spark SQL. When we did our search we found that in future release of Spark this would be added. It would be great if you can tell us which release of Spark would address the same.
as for
3) Spark 1.1 provides better support for SparkSQL ThriftServer interface, which you may want to use for JDBC interfacing. Hive JDBC clients that support v. 0.12.0 are able to connect and interface with such server.
4) Spark 1.1 also provides a SparkSQL CLI interface that can be used for entering queries. In the same fashion that Hive CLI or Impala Shell.
Please, provide more details about what you are trying to achieve for 1 and 2.
I can answer (1):
Apache Sqoop was made specifically to solve this problem for the relational databases. The tool was made for HDFS, HBase, and Hive -- as such it can be used to make data available to Spark, via HDFS and the Hive metastore.
http://sqoop.apache.org/
I believe Cassandra is available to SparkContext via this connector from DataStax: https://github.com/datastax/spark-cassandra-connector -- which I have never used.
I'm not aware of any connector for MongoDB.
1) We have data from various sources( MySQL, Oracle, Cassandra, Mongo)
You have to use different driver for each case. For cassandra there is datastax driver (but i encountered some compatibility problems with SparkSQL). For any SQL system you can use JdbcRDD. The usage is straightforward, look at the scala example:
test("basic functionality") {
sc = new SparkContext("local", "test")
val rdd = new JdbcRDD(
sc,
() => { DriverManager.getConnection("jdbc:derby:target/JdbcRDDSuiteDb") },
"SELECT DATA FROM FOO WHERE ? <= ID AND ID <= ?",
1, 100, 3,
(r: ResultSet) => { r.getInt(1) } ).cache()
assert(rdd.count === 100)
assert(rdd.reduce(_+_) === 10100)
}
But notion that it's just an RDD, so you should work with this data through map-reduce api, not in SQLContext.
Does there exist any utility which we can use?
There is Apache Sqoop project but it's in active development state. The current stable version even doesn't save files in parquet format.
Spark SQL is a capability of the Spark framework. It shouldn't be compared to Shark because Shark is a service. (Recall that with Shark, you run a ThriftServer that you can then connect to from your Thrift app or even ODBC.)
Can you elaborate on what you mean by "get this data into Spark SQL"?
There are a couple of Spark - MongoDB connectors:
- the mongodb connector for hadoop (which doesn't actually need Hadoop at all!) https://databricks.com/blog/2015/03/20/using-mongodb-with-spark.html
the Stratio mongodb connector https://github.com/Stratio/spark-mongodb
If your data is huge and need to perform a lot of transformations then Spark SQL can be used for ETL purpose, else presto could solve all your problems. Addressing your queries one by one:
As your data is in MySQL, Oracle, Cassandra, Mongo all these can be integrated in Presto as it has connectors https://prestodb.github.io/docs/current/connector.html for all these databases.
Once you install Presto in cluster mode you can query all these databases together in one platform, which also provides to join a table from Cassandra and other tables from Mongo, this flexibility is unparalleled.
Presto can be used to connect to Apache Superset https://superset.incubator.apache.org/ which is open source and provides all sets Dashboarding. Also Presto can be connected to Tableau.
You can install MySQL workbench with presto connecting details which helps in providing a UI for all your databases at one place.