I am newbie to apache spark.
My requirement is, when user clicks on the Web UI, query needs to pass to the Spark cluster and get the data back from the cluster and update the UI.
I want to know, how to pass the Spark SQL query and get the result set ?
Spark has Thrift server for this(running SQL queries through JDBC/ODBC). If you are using Java is your middle layer, use JDBC and connect spark Thrift server as like data base and pass/run what ever SQL(supports Spark).
Usually you would have to write a web application, usually with a REST interface, and implement the Spark SQL inside of the server-side REST handler.
You can use Apache Livy.
Details : https://livy.incubator.apache.org/
Related
There is a lot of information about kappa architecture in the internet and after going through some of the conceptual aspects I am trying to drill down to something more concrete. As I main source I used this website.
Let's imaging you want to implement a kappa architecture involving the following tech stack:
Apache Kafka
Apache Spark
Apache Superset
Now imagine the application you want to build do data-analytics against has a PostgreSQL database. Of course you can easily directly connect apache superset with the PostgresSQL database and create charts.
But now you want to see how you would do this with a kappa architecture and you add kafka and spark.
You can emit events to kafka and you can read such events in apache spark. Kafka will retain messages for topcis a certain period as pointed out in the answers to this quesition. When I read about connecting superset with spark in the docs it says hive should be used as a connector (also the project websites states the tool is unsupported, and if you look at this issue on pyhive then you find impyla could be an alternative). But apache hive is a completely different project for a storage system. So how would this connection work?
Assuming you have kafka nodes running (with zookeper obviously) and also have spark running and then you connect apache superset through this hive connector with spark.
How can you write queries against the data that is in kafka (which is in fact the live data)?
On spark side itself you can easily write a scala program that reads data from kafka and does something with it but how can you achieve this from apache superset?
Or is this not the intended way of connecting the things?
If I understood your question, you'd need to use Spark Structured Streaming to register a streaming SQL table into the Hive metastore, which could be queried from Superset from the Spark Thiftserver.
Hive itself doesn't store any of the data. Hive also has a built-in Kafka query handler, so Spark isn't completely necessary.
But, Hive/Spark isn't the only option. You could use Spark to write to HDFS/S3 and have Presto query that from Superset.
Or you can remove Spark and use Kafka Connect write to any other thing that a dashboarding tool (Tableau is another popular one) can support - JDBC database (i.e. Postgres), Mongo, Cassandra, etc. Then you'd just refresh the panels to run a new query.
I have a question regarding a specific spark application usage.
So I want our Spark application to run as a REST API Server, like Spring Boot Applications, therefore it will not be a batch process, instead we will load the application and then we want to keep the application live (no call to spark.close()) and to use the application as Realtime query engine via some API which we will define. I am targeting to deploy it to Databricks. Any suggestions will be good.
I have checked Apache Livy, but not sure whether it will be good option or not.
Any suggestions will be helpful.
Spark isn't designed to run like this; it has no REST API server frameworks other than the HistoryServer and Worker UI built-in
If you wanted a long-running Spark action, then you could use Spark Streaming and issue actions to it via raw sockets, Kafka, etc. rather than HTTP methods
Good question let's discuss step by step
You can create it and it's working fine , following is example :
https://github.com/vaquarkhan/springboot-microservice-apache-spark
I am sure you must be thinking to create Dataset or Data frame and keep into memory and use as Cache (Redis,Gemfire etc ) but here is catch
i) If you have data in few 100k then you really not needed Apache Spark power Java app is good to return response really fast.
ii) If you have data in petabyte then loading into memory as dataset or data frame will not help as Apache Spark doesn’t support indexing since Spark is not a data management system but a fast batch data processing engine, and Gemfire you have flexibility to add index to fast retrieval of data.
Work Around :
Using Apache Ignite’s(https://ignite.apache.org/) In-memory indexes (refer Fast
Apache Spark SQL Queries)
Using data formats that supports indexing like ORC, Parquet etc.
So Why not use Sparing application with Apache Spark without using spark.close().
Spring application as micro service you need other services either on container or PCF/Bluemix/AWS /Azure/GCP etc and Apache Spark has own world and need compute power which is not available on PCF.
Spark is not a database so it cannot "store data". It processes data and stores it temporarily in memory, but that's not presistent storage.
Once Spark job submit you have to wait results in between you cannot fetch data.
How to use Spark with Spring application as Rest API call :
Apache Livy is a service that enables easy interaction with a Spark cluster over a REST interface. It enables easy submission of Spark jobs or snippets of Spark code, synchronous or asynchronous result retrieval, as well as Spark Context management, all via a simple REST interface or an RPC client library.
https://livy.apache.org/
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
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
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.