I have been doing a research about Configuring Spark JobServer Backend (SharedDb) with Cassandra.
And I saw in the SJS documentation that they cited Cassandra as one of the Shared DBs that can be used.
Here is the documentation part:
Spark Jobserver offers a variety of options for backend storage such as:
H2/PostreSQL or other SQL Databases
Cassandra
Combination of SQL DB or Zookeeper with HDFS
But I didn't find any configuration example for this.
Would anyone have an example? Or can help me to configure it?
Edited:
I want to use Cassandra to store metadata and jobs from Spark JobServer. So, I can hit any servers through a proxy behind of these servers.
Cassandra was supported in the previous versions of Jobserver. You just needed to have Cassandra running, add correct settings to your configuration file for Jobserver: https://github.com/spark-jobserver/spark-jobserver/blob/0.8.0/job-server/src/main/resources/application.conf#L60 and specify spark.jobserver.io.JobCassandraDAO as DAO.
But Cassandra DAO was recently deprecated and removed from the project, because it was not really used and maintained by the community.
I have a large, but simple Cassandra database on a Datastax 4.6 cluster. The license renewal is prohibitive for this very simple use case and I am trying to migrate to either a straight Apache or Datastax Comunity version. First is it possible to do an inline update?
I have altered all the keyspaces to remove the "EverywhereStrategy" replication strategy but I still get an error that the DSC version of cassandra I'm trying to get to join the cluster doesn't support it. I'm using Like Cassandra versions (2.0.16) and most other things seem to be close.
java.lang.RuntimeException: org.apache.cassandra.exceptions.ConfigurationException: Unable to find replication strategy class 'org.apache.cassandra.locator.EverywhereStrategy'
If it's not possible to do an inline upgrade what would be the best strategy to migrate a decent size (30 node, 150Tb) cluster?
So to make this work you have to extract any of the DSE features that you may have on any of your tables.
This meant I had to change the replication strategy on the dse_system table from EverywhereStrategy to SimpleStrategy with RF=3 (or almost anything after conversion you can drop this keyspace) The error message was:
java.lang.RuntimeException: org.apache.cassandra.exceptions.ConfigurationException: Unable to find replication strategy class 'org.apache.cassandra.locator.EverywhereStrategy'
I Also had to drop the unused CFS keyspaces. We never used the hadoop/CFS integration so we had nothing in these keyspaces anyway. I didn't capture the error for this.
We did have a solr index on a table we were testing on this cluster about a year ago so I had to drop this columnfamily. The error message was:
java.lang.RuntimeException: java.lang.ClassNotFoundException: com.datastax.bdp.search.solr.Cql3SolrSecondaryIndex
There may be other incompatibilities if you use other features of Datastax Enterprise that you would have to remove, but this was enough for me to get the migration working.
dse-core.jar contains the EverywhereStrategy class.
We solved this problem by doing the following:
Replace everything except the above JAR so nodes can come up fine. Once all nodes are migrated to OSS, drop the dse_system keyspace (that uses this replication), delete the JAR and restart the nodes one by one.
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.
I am working for a small concern and very new to apache cassandra. Studying about cassandra and performing some small analytics like sum function on cassandra DB for creating reports. For the same, Hive and Accunu can be choices.
Datastax Enterprise provides the solution for Apache Cassandra and Hive Integration. Is Datastax Enterprise is the only solution for such integration. Is there any way to resolve the hive and cassandra integration. If so, Can I get the links or documents regarding the same. Is that possible to work the same with the windows platform.
Is any other solution to perform analytics on cassandra DB?
Thanks in advance .
I was trying to download DataStax Enterprise (DSE) for Windows but found there is no such option on their website. I suppose they do not support DSE for Windows.
Apache Cassandra does have builtin Hadoop support. You need to set up a standalone Hadoop cluster colocated with Apache Cassandra nodes and then use ColumnFamilyInputFormat and ColumnFamilyOutputFormat to read/write data from/to your Hadoop cluster.
I'm experimenting with Datastax Enterprise and I'm trying to have a cluster that mixes Enterprise nodes and standard Cassandra community nodes. I would only need a few nodes with advanced features like Solr and it would be nice to have all the nodes in the same cluster.
I tried to bootstrap a community node to a test Enterprise cluster, and it couldn't join the ring properly, throwing exceptions like that:
Unable to find compaction strategy class
'com.datastax.bdp.hadoop.cfs.compaction.CFSCompactionStrategy'
I assume that the Enterprise node tries to replicate CFs that have features from DSE, which are not recognized by the community node.
Is there a way to prevent that from happening? Am I trying to do something that's not possible/supported/allowed by DSE?
That is an unsupported configuration. The full cluster needs to be installed with DataStax enterprise binaries on all nodes. You can choose which nodes run as vanilla Cassandra, Hadoop or Solr by startup options on each node. DSE has a custom compaction strategy and snitch so that error is expected.