Is there a way to reduce the size of the data collected? The full table selected is just 987 MB, but when the sql warehouse tries to read > collect > send to tableau the collect process is resulting in over 32GB.
java.lang.RuntimeException: [Simba][Hardy] (35) Error from server: error code: '0' error message: 'Error running query: org.apache.spark.SparkException: Job aborted due to stage failure: Total size of serialized results of 196 tasks (32.8 GB) is bigger than spark.driver.maxResultSize (32.0 GB)'.
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This tutorial explains how to create a simple Tableau Software dashboard based on Cassandra data. The tutorial uses the Spark ODBC driver to integrate Cassandra and Apache Spark. Data and step-by-step instructions for installation and setup of the demo are provided.
The Spark SQL Thrift server is a JDBC/ODBC server allowing JDBC and ODBC interfaces for client connections like Tableau to Spark (and then to Cassandra). See here for more details _enterprise/4.8/datastax_enterprise/spark/sparkSqlThriftServer.html.
The IP address is the address of your Spark Master node. You may need to replace 127.0.0.1 with your instance IP address if you are not running Spark cluster or DSE locally. With DSE, you can run the command "dsetool sparkmaster" to find your Spark Master node IP.
Note that to connect Tableau Software to Apache Cassandra we would have been able to connect directly via the DataStax ODBC driver. But in this case all computations, joins, aggregates are done on the client side, so it's not efficient and risky for large dataset. On the contrary, with Spark jobs everything is done on the server side and on a distributed manner.
Then you should be able to see all Apache Cassandra keyspaces (named Schema in Tableau interface) and tables (click enter in Schema and Table inputs to see all available Cassandra keyspaces and tables).
Change the inner join clause with right columns from the 2 tables, Performer from albums table and Name from performers table (click on the blue part of the link between the 2 tables to be able to edit this inner join).
I have been successful in integrating Tableau with Spark Thrift server using Samba ODBC. I have tried using the cache table during the initial SQL and the performance has been great till now. I am now looking for a way to cache and un cache few of the frequently used tables when they are updated using through our data pipelines.
The challlenge that I am facing is that the cache table done via Tableau will remain in cache through the lifetime of the thrift server but when I write my data pipleline process and submit spark jobs it will use a different spark context.
Spark Thrift Server (STS) runs its SparkContext within the STS JVM daemon. That Spark Context is not available to external clients (with or without spark submit). The only way to access that Spark context is via the JDBC connection to STS. After your external processing has completed, you could submit a cache refresh operation to your STS.
Driver Version: V1.0.9.1009Running connectivity tests...Attempting connectionFailed to establish connectionSQLSTATE: HY000[Simba][ThriftExtension] (5) Error occurred while contacting server: No more data to read.. This could be because you are trying to establish a non-SSL connection to a SSL-enabled serverTESTS COMPLETED WITH ERROR.
In Tableau there is a Spark connector which I used to connect to the Spark Thrift Server. Looking at your error message it seems to me that your application is not able to reach the spark thrift server instance. Can you do a telnet ?
Hi All - We have implemented the solution as explained by bikas. At the end of our batch processing we are invoking a process which refreshes the cache inside Spark through beeline server. The tableau dashboard also connects to the same instance of beeline server and therefore is able to get the updated data in-memory. Currently, we are looking at high availability of the cache in case one of the STS goes down.
Dont forget to uncache the old data Also, each STS has its own SparkContext which will be lost if that STS is lost. So there is no way currently to have availability of the cache inside an STS if that STS goes down. Having 2 identical STS instances with identical caches is possibly the only solution. Assuming your cache creation code is consistent.
Thanks bikas....:) I am doing what you mentioned..I have two STS instances on two separate servers and then the caching is being done on both the instances. Quick question when I run CACHE TABLE TABLE_NAME will it first un-cache and then cache the data?
Tableau is a very popular data exploration tool that we love and that works nicely with Lentiq. The query engine is SparkSQL which uses Spark's in-memory mechanisms and query planner to execute SQL queries on data.
Tableau uses database drivers to connect to the various data sources. We will be using a driver called Simba Spark ODBC Driver to connect to our SparkSQL query engine, for both Mac and Windows. This driver is now shipped with Tableau starting 10.2 - 2019.1.3 version. If you use an older version follow the steps below. Otherwise, skip to step 3.
Add tables of interest to your tableau sheets. If the tables do not appear in the list below click on the magnifying glass icon or type in the name of the table as it appears in Lentiq's table browser.
You can now browse and work with data in all tables in the data lake. The performance of the entire system is heavily dependent on the amount of resources you allocate to your SparkSQL query engine. Since spark caches data in RAM the very first interactions will be slower but subsequent ones should be fast.
Ballista has a scheduler and an executor process that are standard Rust executables and can be executed directly, butDockerfiles are provided to build images for use in containerized environments, such as Docker, Docker Compose, andKubernetes. See the deployment guide for more information
SQL and DataFrame queries can be submitted from Python and Rust, and SQL queries can be submitted via the ArrowFlight SQL JDBC driver, supporting your favorite JDBC compliant tools such as DataGripor tableau. For setup instructions, please see the FlightSQL guide.
Ballista is designed from the ground up to use columnar data, enabling a number of efficiencies such as vectorizedprocessing (SIMD and GPU) and efficient compression. Although Spark does have some columnar support, it is stilllargely row-based today.
The combination of Rust and Arrow provides excellent memory efficiency and memory usage can be 5x - 10x lower thanApache Spark in some cases, which means that more processing can fit on a single node, reducing the overhead ofdistributed compute.
In my organization we'll be accessing BQ through a third party tool like Tableau over JDBC/ODBC connection , One of the key optimization lever for BQ is to run the query in Dry run mode but that's only doable through console , Is it feasible to run BQ in Dry run from 3rd party tools like tableau?
At the highest level, my belief is that the answer to your question is "no". Here is my thinking. BigQuery allows you to execute SQL queries against it. BigQuery provides an API that you can use to submit those queries. One of the parameters of those queries is "dryRun" which can be true/false. (see here). What this much tells me is that if you are making your own API calls to BigQuery using the Google BigQuery API then you have full control over specifying dry run. However, your question was about 3rd party products such as Tableau. Now things get more subtle ... for a 3rd party product, it will still have to make a query call to BigQuery and exactly how the product makes the BigQuery call will give us an indication on the overall answer.
I see two possibilities:
1. The 3rd party application makes BigQuery API calls directly. In this case, it would be up to the 3rd party product whether or not it exposes a mechanism to enable "dry run". Technically, it could ... but it would be up to that product to determine how flag would be switched on.
2. The 3rd party application makes BigQuery API calls indirectly through JDBC/ODBC. In this case, you are likely using the Magnitude Simba drivers described here. I looked at both JDBC and ODBC and searched for "dry run" in their detailed specs and found nothing. That seems to tell me that they have no "special" switches to enable dry run execution.
Putting all this together, I'm going to guess that you can't make a dry run call from a 3rd party app unless that app explicitly makes BigQuery API calls itself and provides explicit enablement for making dry-run requests.
Once you connected third party tool to BigQuery and provided dataset , it will execute query and fetch data. Before connecting you can see query in cloud console if possible else we have another option to check query job usage , this is not actual dry run but you can analyse data fetched by your Tableau or BI Dashboard. In below query result check out for label_value column. 152ee80cbc
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