Project: YugabyteDB (App Definition and Development / Database)
File: python-ysql.md

title: Build Python applications using Apache Spark and YugabyteDB YSQL headerTitle: Build a Python application using Apache Spark and YugabyteDB linkTitle: YSQL description: Learn how to build a Python application using Apache Spark and YugabyteDB YSQL menu: preview_integrations: identifier: apache-spark-3-python-ysql parent: apache-spark weight: 575 type: docs <ul class="nav nav-tabs-alt nav-tabs-yb"> <li > <a href="../java-ysql/" class="nav-link"> <i class="fa-brands fa-java" aria-hidden="true"></i> Java </a> </li> <li > <a href="../scala-ysql/" class="nav-link"> <i class="icon-scala" aria-hidden="true"></i> Scala </a> </li> <li > <a href="../python-ysql/" class="nav-link active"> <i class="icon-python" aria-hidden="true"></i> Python </a> </li> <li > <a href="../spark-sql/" class="nav-link"> Spark SQL </a> </li> </ul> The following tutorial describes how to use Python's Spark API `pyspark` with YugabyteDB, and perform YSQL queries. This tutorial assumes that you have: YugabyteDB running. If you are new to YugabyteDB, follow the steps in . Java Development Kit (JDK) 1.8. JDK installers for Linux and macOS can be downloaded from , , or . Homebrew users on macOS can install using `brew install AdoptOpenJDK/openjdk/adoptopenjdk8`. . From your spark installation directory, use the following command to start `pyspark` shell, and pass the YugabyteDB driver package with the `--packages` parameter. The command fetches the YugabyteDB driver from local cache (if present), or installs the driver from . ```sh ./bin/pyspark --packages com.yugabyte:jdbc-yugabytedb:42.3.0 ``` The Spark session should be available as follows: ```output Welcome to / / _ _/ / \ \/ \/ `/ / '/ / / ./\,// //\_\ version 3.3.0 /_/ Using Python version 3.8.9 (default, Jul 19 2021 09:37:30) Spark context Web UI available at http://192.168.0.141:4040 Spark context available as 'sc' (master = local[*], app id = local-1658425088632). SparkSession available as 'spark'. >> ``` Create the database and table you will read and write to as follows: From your YugabyteDB installation directory, use shell to read and write directly to the database as follows: ```sh ./bin/ysqlsh ``` Create a database `ysql_pyspark` and connect to it using the following: ```sql yugabyte=# CREATE DATABASE ysql_pyspark; yugabyte=# \c ysql_pyspark; ``` ```output You are now connected to database "ysql_pyspark" as user "yugabyte". ysql_pyspark=# ``` Create a table in the `ysql_pyspark` database to read and write data through the JDBC connector, and populate it with data as follows: ```sql ysqlpyspark=# create table test as select generateseries(1,100000) AS id, random(), ceil(random() * 20); ``` From your Spark prompt, set up the connection URL and properties to read and write data through the JDBC connector to YugabyteDB. ```spark >> jdbcUrl = "jdbc:yugabytedb://localhost:5433/ysql_pyspark" >> connectionProperties = { "user" : "yugabyte", "password" : "yugabyte", "driver" : "com.yugabyte.Driver" } ``` To read and write data using the JDBC connector, create a in one of the following ways: Create a DataFrame for the `test` table to read data via the JDBC connector using the following: ```spark >> test_Df = spark.read.jdbc(url=jdbcUrl, table="test", properties=connectionProperties) ``` Alternatively, you can use SQL queries to create a DataFrame which pushes down the queries to YugabyteDB through the JDBC connector to fetch the rows, and create a DataFrame for that result. ```spark >> testDf = spark.read.jdbc(url=jdbcUrl, table="(select * from test) testalias",properties=connectionProperties) ``` Output the schema of the DataFrame created as follows: ```spark >> test_Df.printSchema() ``` ```output root |-- id: integer (nullable = true) |-- random: double (nullable = true) |-- ceil: double (nullable = true) ``` Read some data from the table using the DataFrame APIs: ```scala >>

``` ```output +--++ |ceil | sum(id)| +--++ | 8.0|248688663| | 7.0|254438906| | 18.0|253717793| | 1.0|253651826| | 4.0|251144069| | 11.0|252091080| | 14.0|244487874| | 19.0|256220339| | 3.0|247630466| | 2.0|249126085| +--++ ``` Another alternative is to use the `spark.sql()` API to directly execute SQL queries using the following code: ```spark >> test_Df.createOrReplaceTempView("test") >> res_df = spark.sql("select ceil, sum(id) from test group by ceil limit 10") >> res_df.show() ``` The output will be similar to . The following Spark query renames the column of the table `test` from `ceil` to `roundoff` in the DataFrame, then creates a new table with the schema of the changed DataFrame, inserts all its data in the new table, and names it as `testcopy` using the JDBC connector. ```spark >> spark.table("test").withColumnRenamed("ceil", "roundoff").write.jdbc(url=jdbcUrl, table="testcopy", properties=connectionProperties) ``` Verify that the new table `test_copy` is created with the changed schema, and all the data from `test` is copied to it using the following commands from your ysqlsh terminal: ```sql ysql_pyspark=# \dt ``` ```output List of relations Schema | Name | Type | Owner --+--+-+- public | test_copy | table | yugabyte public | test | table | yugabyte (2 rows) ``` ```sql ysqlpyspark=# \d testcopy ``` ```output Table "public.test_copy" Column | Type | Collation | Nullable | Default --++--+-+ id | integer | | | random | double precision | | | round_off | double precision | | | ``` ```sql ysqlpyspark=# SELECT COUNT(*) FROM testcopy; ``` ```output count -- 100000 (1 row) ``` Use the `append` , to append data from `test_copy` to the `test` table as follows: ```spark >> testcopyDf = spark.read.jdbc(url=jdbcUrl, table="(select * from testcopy) testcopy_alias", properties=connectionProperties) >> testcopyDf.createOrReplaceTempView("test_copy") >> spark.table("test_copy").write.mode("append").jdbc(url=jdbcUrl, table="test", properties=connectionProperties) ``` Verify the changes using ysqlsh: ```sql ysql_pyspark=# SELECT COUNT(*) FROM test; ``` ```output count -- 200000 (1 row) ``` To maintain parallelism while fetching the table content, create a DataFrame for the table `test` with some specific options as follows: ```spark >> newtestdf = spark.read.jdbc(url=jdbcUrl, table="test", properties=connectionProperties,numPartitions=5, column="ceil", lowerBound=0, upperBound=20) >> newtestdf.createOrReplaceTempView("test") >> spark.sql("select sum(ceil) from test where id > 50000").show() ``` ```output ++ |sum(ceil)| ++ |1049414.0| ++ ``` The options used in the example help in breaking down the whole task into `numPartitions` parallel tasks on the basis of the `partitionColumn`, with the help of minimum and maximum value of the column; where, `numPartitions` - divides the whole task to `numPartitions` parallel tasks. `lowerBound` - minimum value of the `partitionColumn` in a table. `upperBound` - maximum value of the `partitionColumn` in a table. `partitionColumn` - the column on the basis of which a partition occurs. Additionally, the following two options help in optimizing the SQL queries executing on this dataframe if those SQL queries consist of some filters or aggregate functions by pushing down those filters and aggregates to the YugabyteDB through the JDBC connector. `pushDownPredicate` - optimizes the query by pushing down the filters to YugabyteDB through the JDBC connector. `pushDownAggregate` - optimizes the query by pushing down the aggregated to YugabyteDB through the JDBC connector. To verify that the Spark job is created, do the following: Navigate to the Spark UI using <https://localhost:4040>. If your port 4040 is in use, then change the port to the one mentioned when you started the shell. From the SQL/DataFrame tab, click the last executed SQL statement to see if `numPartitions=5` is displayed as shown in the following image: