Does Spark (Specifically Spark 3.X) support the idea of federated queries via persisted views or tables? We do a lot of the "JDBC to other sources" stuff in our ETL, so I am familiar with how to make a JDBC connection and even create a temporary view of a JDBC connection, if needed. I am more interested in persisting that table or view within the metastore, so I can query it as a live connection, for as long as I want. Something similar to how Presto or Denodo does it.
We do most of our stuff in Azure Databricks also, if anyone knows if they offer something that vanilla Spark would not.
Anyone know if that is possible? I haven't had much luck finding anything yet.
Thanks!
Related
Which option is better to use, spark as an execution engine on hive or accessing hive tables using spark SQL? And Why?
A few assumptions here are:
Reason to opt for SQL is to stay user friendly, e.g. if you have business users trying to access data.
Hive is in consideration because it provides an SQL like interface and persistence of data
If that is true, Spark-SQL is perhaps the better way forward. It is better integrated within Spark and as an integral part of Spark, it will provide more features (one example is structured streaming). You will still get user friendliness and an SQL like interface to Spark so you will get full benefits. But you will need to manage your system only from Spark's point of view. Hive installation and management will still be there but from a single perspective.
Using Hive with Spark as execution engine will keep you limited based upon how good a translation Hive's libraries are able to do to convert your HQL to Spark. They may do a pretty good job but you will still loose the advanced features of Spark SQL. And new features may take longer to get integrated in Hive compared to Spark SQL.
Also, with Hive exposed to end users, some advanced users or data engineering teams may want access to Spark. This will cause you to manage two tools. System management may get more tedious compared to only using Spark-SQL in this scenario as Spark SQL has the potential to serve both non-technical and advanced users and even if advanced users use pyspark, spark-shell or more, they will still be integrated within the same toolset.
I am very beginner here. Sorry If I asked duplicate/silly question.
Coming to point, as my product(Java web application) demands, I need to write some application which should push data to any of data stores(based on some configuration). The data store can be RDBMS, Hive or any NoSQL data store. So the query is, is SparkSql is best fit for my case, if yes, can I have list of data stores supported by Spark SQL. If Spark won't do this, are they any other approaches.
Please help me!
Yes! SparkSql(Spark) is the best fit for your usecase.
As per my knowledge, SparkSql supports RDBMS, Hive, and any NoSQL data store.
SparkSQL may not have APIs to directly access few stores but with a little help from Spark's API, you should be able to connect any data store.
We have been using Spark to connect to RDBMS, Cassandra, HBase, ElasticSearch, Solr, Hive, S3, HDFS etc.
Here is the short story:
A BI tool (PowerBI) connects to Spark cluster and uses HiveThriftServer2 application to get aggregated data via hive queries.
However, each query takes a lot of time since every time it reads data from files. I would like to cache my table in this application and looking for the way to send query "cache table myTable" through same channel, so next queries will run quick.
What would be a solution to send hive query to specific application? If it matters, the application is a thrift service of Spark.
Thanks a lot!
Looks like I succeed to do it, by installing Spark Odbc driver and using it to connect to thift server and send the sql query "cache table xxx". I wonder if there is more elegant way
I have been evaluating Hadoop on azure HDInsight to find a big data solution for our reporting application. The key part of this technology evaluation is that the I need to integrate with MSSQL Reporting Services as that is what our application already uses. We are very short on developer resources so the more I can make this into an engineering exercise the better. What I have tried so far
Use an ODBC connection from MSSQL mapped to the Hive on HDInsight.
Use an ODBC connection from MSSQL using HBASE on HDInsight.
Use SPARKQL locally on the azure HDInsight Remote desktop
What I have found is that HBASE and Hive are far slower to use with our reports. For test data I used a table with 60k rows and found that the report on MSSQL ran in less than 10 seconds. I ran the query on the hive query console and on the ODBC connection and found that it took over a minute to execute. Spark was faster (30 seconds) but there is no way to connect to it externally since ports cannot be opened on the HDInsight cluster.
Big data and Hadoop are all new to me. My question is, am I looking for Hadoop to do something it is not designed to do and are there ways to make this faster?I have considered caching results and periodically refreshing them, but it sounds like a management nightmare. Kylin looks promising but we are pretty married to windows azure, so I am not sure that is a viable solution.
Look at this documentation on optimizing Hive queries: https://azure.microsoft.com/en-us/documentation/articles/hdinsight-hadoop-optimize-hive-query/
Specifically look at ORC and using Tez. I would create a cluster that has Tez on by default and then store your data in ORC format. Your queries should be much more performant then.
If going through Spark is fast enough, you should consider using the Microsoft Spark ODBC driver. I am using it and the performance is not comparable to what you'll get with MSSQL, other RDBMS or something like ElasticSearch but it does work pretty reliably.
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.