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
Related
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
I have created a table, ztest7 in the default database in my hive. I am able to query it using beeline. In tableau, I can query it using a custom sql.
However the table does NOT show when I search for it.
Am I missing something here?
Tableau Desktop Version = v10.1.1
Hive = v2.0.1
Spark = v2.1.0
Best Regards
I have the same issue with Tableau Desktop 10 (mac) to Hive (2.1.1) via Spark SQL 2.1 (on centos 7 server)
This is what I got from Tableau Support:
In Tableau Desktop, the ability to connect to Spark SQL without a
defining a default schema is not currently built into the product.
As a preliminary step, to define a default schema, configure the Spark
SQL hivemetastore to utilize a SchemaRDD or DataFrame. This must be
defined in the Hive Metastore for Tableau Desktop to be able to access
it. Pure schema-less Spark RDD's can not be queried by Spark SQL
because of the lack of a schema. RDDs can be converted into
SchemaRDDs, which have additional schema metadata as Spark SQL
provides access to SchemaRDDs. When a SchemaRDD is created, it is only
available in the local namespace or context, and is unavailable to
external services accessing Spark through ODBC and the Spark Thrift
Server. For Tableau to have access, the SchemaRDD needs to be
registered in a catalog that is available outside of just the local
context; the Hive Metastore is currently the only supported service.
I don't know how to check/implement this.
PS: I'd have posted this as a comment because I am not allowed to as I am new to Stack Overflow.
In the file labeled Table on the left side of the screen, Try selecting contains, entering part of your table name and hitting enter
I ran into similar issue. In my case, I had loaded tables using HIVE but the tableau connection to the data source was made using Impala as shown in the image below.
To fix the issue of not seeing the tables in tableau dropdown, try running INVALIDATE METADATA database.table_name in the impala interface. This fixed the problem for me.
To know why this fixes the issue, refer this link.
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/
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.