I'm looking for, with no success, how to read a Azure Synapse table from Scala Spark. I found in https://learn.microsoft.com connectors for others Azure Databases with Spark but nothing with the new Azure Data Warehouse.
Does anyone know if it is possible?
It is now directly possible, and with trivial effort (there is even a right-click option added in the UI for this), to read data from a DEDICATED SQL pool in Azure Synapse (the new Analytics workspace, not just the DWH) for Scala (and unfortunately, ONLY Scala right now).
Within Synapse workspace (there is of course a write API as well):
val df = spark.read.sqlanalytics("<DBName>.<Schema>.<TableName>")
If outside of the integrated notebook experience, need to add imports:
import com.microsoft.spark.sqlanalytics.utils.Constants
import org.apache.spark.sql.SqlAnalyticsConnector._
It sounds like they are working on expanding to SERVERLESS SQL pool, as well as other SDKs (e.g. Python).
Read top portion of this article as reference: https://learn.microsoft.com/en-us/learn/modules/integrate-sql-apache-spark-pools-azure-synapse-analytics/5-transfer-data-between-sql-spark-pool
maybe I misunderstood your question, but normally you would use jdbc connection in Spark to use data from remote database
check this doc
https://docs.databricks.com/data/data-sources/azure/synapse-analytics.html
keep in mind, Spark would have to ingest data from Synapse tables into memory for processing and perform transformations there, so it is not going to push down operations into Synapse.
Normally, you want to run SQL query against source database and only bring results of SQL into Spark dataframe.
Related
I'm looking at using both Databricks and Snowflake, connected by the Spark Connector, all running on AWS. I'm struggling to understand the following before triggering a decision:
How well does the Spark Connector perform? (performance, extra costs, compatibility)
What comparisons can be made between Databricks SQL and Snowflake SQL in terms of performance and standards?
What have been the “gotchas” or unfortunate surprises about trying to use both?
Snowflake has invested in the Spark connector's performance and according to benchmarks[0] it performs well.
The SQL dialects are similar. "Databricks SQL maintains compatibility with Apache Spark SQL semantics." [1] "Snowflake supports most of the commands and statements defined in SQL:1999." [2]
I haven't experienced gotchas. I would avoid using different regions. The performance characteristics of DataBricks SQL are different since 6/17 when they made their Photon engine default.
As always, the utility will depend on your use case, for example:
If you were doing analytical DataBricks SQL queries on partitioned compressed Parquet DeltaLake, then the performance ought to be roughly similar to Snowflake -- but if you were doing analytical DataBricks SQL queries against a JDBC MySQL connection then performance of Snowflake should be vastly better.
If you were doing wide table scan style queries (e.g. select * from foo (no where, no limit)) in DataBricks SQL and then doing analysis in a kernel (or something) then switching to Snowflake isn't going to do much for you.
etc
[0] - https://www.snowflake.com/blog/snowflake-connector-for-spark-version-2-6-turbocharges-reads-with-apache-arrow/
[1] - https://docs.databricks.com/sql/release-notes/index.html
[2] - https://docs.snowflake.com/en/sql-reference/intro-summary-sql.html
Using Python-3, I am trying to compare an Excel (xlsx) sheet to an identical spark table in Databricks. I want to avoid doing the compare in Databricks. So I am looking for a way to read the spark table via the Databricks api. Is this possible? How can I go on to read a table: DB.TableName?
There is no way to read the table from the DB API as far as I am aware unless you run it as a job as LaTreb already mentioned. However, if you really wanted to, you could use either the ODBC or JDBC drivers to get the data through your databricks cluster.
Information on how to set this up can be found here.
Once you have the DSN set up you can use pyodbc to connect to databricks and run a query. At this time the ODBC driver will only allow you to run Spark-SQL commands.
All that being said, it will probably still be easier to just load the data into Databricks, unless you have some sort of security concern.
I can recomend you write pyspark code in notebook, call the notebook from previously defined job, and establish connection between your local machine and databricks workspace.
You could perfom comaprision directly on spark or convert data frames to pandas if you wish. If noteebok will end comaprision, could retrun result from particular job. I think that sending all databricks tables could be impossible because of API limitation you have spark cluster to perform complex operation, API should be use to send small messages.
Officical documentation:
https://learn.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/jobs#--runs-get-output
Retrieve the output and metadata of a run. When a notebook task
returns a value through the dbutils.notebook.exit() call, you can use
this endpoint to retrieve that value. Azure Databricks restricts this
API to return the first 5 MB of the output. For returning a larger
result, you can store job results in a cloud storage service.
I have 2 questions w.r.t spark and Snowflake datawarehouse.
1) Is there any way to query/create snowflake tables like hive/spark(either new or old versions of spark)
val hive_tables=hiveContext.sql("show tables").foreach(println)
2) hiveContext.sql("create table....")
first question is about knowing what tables are present for that particular user for the particular role. The reason why I am asking question is via web ui of snowflake I am able to query the table but through spark I am not able to query
Exception in thread "main" net.snowflake.client.jdbc.SnowflakeSQLException: SQL compilation error:
Object 'mytable' does not exist.
You should double check things like database/schema/role in your JDBC connection settings. If you don't see a table via JDBC, one of these might be the culprit.
You can validate the current settings by running e.g. show roles, show schemas and show databases on the established JDBC connection.
In general, I highly recommend using Spark-Snowflake connector for communicating with Snowflake from Spark. It also provides Utils.runQuery() for running simple queries like DDL.
I just got this question while designing the storage part for a Hadoop-based platform. If we want to have data scientists to have access to the tables which have already been stored in a relational database (e.g.SQL-server of a Azure Virtual Machine), then will there be any particular benefits if we import the tables from SQL-server to HDFS (e.g. WASB) and create Hive tables on top of them?
In other words, since Spark allows users to read data from other databases using JDBC,is there any performance improvement if we persist the tables from the database in appropriate format (avro, parquet etc.) in HDFS and use SparkSQL to access them using HQL?
I am sorry if this question has been asked, I have done some research but could not get a comparison between the two methodologies.
I think there will be a big performance improvement as the data is local (assuming Spark is running on same Hadoop cluster where the data is stored on HDFS). Using JDBC if the actions/processing performed is interactive then user has to wait for the data to be loaded through JDBC from another machine (N/W latency and IO throughput) whereas if that is done upfront then user (data scientist) can concentrate on performing the actions straight away.
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.