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Get the Report →How to connect and process Elasticsearch Data from Azure Databricks
Use CData, Azure, and Databricks to perform data engineering and data science on live Elasticsearch Data
Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Elasticsearch data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Elasticsearch data in Databricks.
With built-in optimized data processing, the CData JDBC driver offers unmatched performance for interacting with live Elasticsearch data. When you issue complex SQL queries to Elasticsearch, the driver pushes supported SQL operations, like filters and aggregations, directly to Elasticsearch and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Elasticsearch data using native data types.
About Elasticsearch Data Integration
Accessing and integrating live data from Elasticsearch has never been easier with CData. Customers rely on CData connectivity to:
- Access both the SQL endpoints and REST endpoints, optimizing connectivity and offering more options when it comes to reading and writing Elasticsearch data.
- Connect to virtually every Elasticsearch instance starting with v2.2 and Open Source Elasticsearch subscriptions.
- Always receive a relevance score for the query results without explicitly requiring the SCORE() function, simplifying access from 3rd party tools and easily seeing how the query results rank in text relevance.
- Search through multiple indices, relying on Elasticsearch to manage and process the query and results instead of the client machine.
Users frequently integrate Elasticsearch data with analytics tools such as Crystal Reports, Power BI, and Excel, and leverage our tools to enable a single, federated access layer to all of their data sources, including Elasticsearch.
For more information on CData's Elasticsearch solutions, check out our Knowledge Base article: CData Elasticsearch Driver Features & Differentiators.
Getting Started
Install the CData JDBC Driver in Azure
To work with live Elasticsearch data in Databricks, install the driver on your Azure cluster.
- Navigate to your Databricks administration screen and select the target cluster.
- On the Libraries tab, click "Install New."
- Select "DBFS" as the Library Source and "JAR" as the Library Type.
- Upload the JDBC JAR file (cdata.jdbc.elasticsearch.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Elasticsearch\lib).
Connect to Elasticsearch from Databricks
With the JAR file installed, we are ready to work with live Elasticsearch data in Databricks. Start by creating a new notebook in your workspace. Name the workbook, make sure Python is selected as the language (which should be by default), click on Connect and under General Compute select the cluster where you installed the JDBC driver (should be selected by default).
Configure the Connection to Elasticsearch
Connect to Elasticsearch by referencing the class for the JDBC Driver and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.
driver = "cdata.jdbc.elasticsearch.ElasticsearchDriver" url = "jdbc:elasticsearch:RTK=5246...;Server=127.0.0.1;Port=9200;User=admin;Password=123456;"
Built-in Connection String Designer
For assistance in constructing the JDBC URL, use the connection string designer built into the Elasticsearch JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.
java -jar cdata.jdbc.elasticsearch.jar
Fill in the connection properties and copy the connection string to the clipboard.
Set the Server and Port connection properties to connect. To authenticate, set the User and Password properties, PKI (public key infrastructure) properties, or both. To use PKI, set the SSLClientCert, SSLClientCertType, SSLClientCertSubject, and SSLClientCertPassword properties.
The data provider uses X-Pack Security for TLS/SSL and authentication. To connect over TLS/SSL, prefix the Server value with 'https://'. Note: TLS/SSL and client authentication must be enabled on X-Pack to use PKI.
Once the data provider is connected, X-Pack will then perform user authentication and grant role permissions based on the realms you have configured.
Load Elasticsearch Data
Once the connection is configured, you can load Elasticsearch data as a dataframe using the CData JDBC Driver and the connection information.
remote_table = spark.read.format ( "jdbc" ) \ .option ( "driver" , driver) \ .option ( "url" , url) \ .option ( "dbtable" , "Orders") \ .load ()
Display Elasticsearch Data
Check the loaded Elasticsearch data by calling the display function.
display (remote_table.select ("OrderName"))
Analyze Elasticsearch Data in Azure Databricks
If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.
remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )
The SparkSQL below retrieves the Elasticsearch data for analysis.
result = spark.sql("SELECT SAMPLE_VIEW.Freight, Customers.ContactName FROM Customers INNER JOIN SAMPLE_VIEW ON Customers.CustomerId=SAMPLE_VIEW.CustomerId")
The data from Elasticsearch is only available in the target notebook. If you want to use it with other users, save it as a table.
remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )
Download a free, 30-day trial of the CData JDBC Driver for Elasticsearch and start working with your live Elasticsearch data in Azure Databricks. Reach out to our Support Team if you have any questions.