I'm writing a Spark-based app and have to drop some tables in Cassandra DB.
I know how to read from tables with spark.read.format("jdbc"). I know how to save dataframe with df.write.format("jbdc").
But how can I drop a table that I don't need anymore?
To drop a Cassandra table, you can simply use the Spark SQL DROP TABLE command:
spark.sql("DROP TABLE table_name")
Note that the JDBC API is limited so our general recommendation is to use the Spark Cassandra connector. It is fully open-source so is free to use.
The Spark Cassandra connector is a library specifically designed for connecting to Cassandra clusters from Spark applications. Cassandra tables are exposed as DataFrames or RDDs and the connector also allows execution of the full CQL API. Cheers!
I have setup datahub and spark in k8s in different namespace, I can run spark with datahub configurations following this guide: https://datahubproject.io/docs/metadata-integration/java/spark-lineage/, my spark application will get data from Minio, then do some transformations including groupby, pivot, rename and a spark SQL query, then write result to cassandra database.
After spark execution finished, I can see my spark application in datahub, but there is no information for "Tasks" or "Lineage" in datahub, what should I do to get these data? There is very limited information in datahub document. Thanks!
I have a Kafka - Spark Streaming application to ingest and process 60K events per min. I need a database to store my transformed dataframes to be accessed by visualization layer. Can Redshift be used for this with Spark Streaming or should Cassandra be used? I will be processing and storing the dataframes in every spark window of 30 seconds. Also I need to read from the datastore in every window. I guess Redhsift is primarily a data warehousing database not for OLTP sort of the processing.. any ideas?
You should check out SnappyData. SnappyData deeply integrates an in-memory database with Spark that allows hybrid OLTP/OLAP applications. You can write Spark Streaming applications on top of Snappy that can update/delete data from the database. Further, because it does not go over a connector, it performs better than the myriad datastores that have Spark connectors and even the native Spark cache. There may be other datastores that offer hybrid OLTP/OLAP applications on Spark in the aforementioned link.
Disclaimer: I am a SnappyData employee.
Now I'm testing Spark SQL like an query engine for Microsoft Power BI.
What I have:
A huge Cassandra table with data I need to analyze.
An Amazon server with 8 cores and 16Gb of RAM.
A Spark Thrift server on this server. Version of Spark - 1.6.1
A Hive table mapped to a huge Cassandra table.
create table data using org.apache.spark.sql.cassandra options (cluster 'Cluster', keyspace 'myspace', table 'data');
All was ok until I tried to connect Power BI to Spark. The problem is that Power BI is trying to fetch all data from huge Cassandra table. Obviously Spark Thrift Server crashes with OOM Error. In this case I cant just add RAM to Spark Thrift Server because Cassandra table with raw data is really huge. Also I cant rely on custom initial query on BI side, because every time user forget about setting this query server would crash.
The best approach I see is in automatically wrapping all queries from BI in some kind of
SELECT * FROM (... BI select ...) LIMIT 1000000
It will be okay for current use cases.
So, is it possible on the server side? How I can do it?
If not, how I can prevent Spark Thrift Server crashes? Is there a possibility to drop or cancel huge queries before getting OOM?
Thanks.
Ok, I find a magic configuration option that solves my problem:
spark.sql.thriftServer.incrementalCollect=true
When this option is set, Spark splits the data that is fetched by a volume-consuming query to chunks
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.