> ## Documentation Index > Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-lginte-1765488813-6406a61.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Spark SQL Toolkit This notebook shows how to use agents to interact with `Spark SQL`. Similar to [SQL Database Agent](/oss/python/integrations/tools/sql_database), it is designed to address general inquiries about `Spark SQL` and facilitate error recovery. **NOTE: Note that, as this agent is in active development, all answers might not be correct. Additionally, it is not guaranteed that the agent won't perform DML statements on your Spark cluster given certain questions. Be careful running it on sensitive data!** ## Initialization ```python theme={null} from langchain_community.agent_toolkits import SparkSQLToolkit, create_spark_sql_agent from langchain_community.utilities.spark_sql import SparkSQL from langchain_openai import ChatOpenAI ``` ```python theme={null} from pyspark.sql import SparkSession spark = SparkSession.builder.getOrCreate() schema = "langchain_example" spark.sql(f"CREATE DATABASE IF NOT EXISTS {schema}") spark.sql(f"USE {schema}") csv_file_path = "titanic.csv" table = "titanic" spark.read.csv(csv_file_path, header=True, inferSchema=True).write.saveAsTable(table) spark.table(table).show() ``` ```output theme={null} Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). 23/05/18 16:03:10 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using built-in Java classes where applicable ``` ```output theme={null} +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ |PassengerId|Survived|Pclass| Name| Sex| Age|SibSp|Parch| Ticket| Fare|Cabin|Embarked| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ | 1| 0| 3|Braund, Mr. Owen ...| male|22.0| 1| 0| A/5 21171| 7.25| null| S| | 2| 1| 1|Cumings, Mrs. Joh...|female|38.0| 1| 0| PC 17599|71.2833| C85| C| | 3| 1| 3|Heikkinen, Miss. ...|female|26.0| 0| 0|STON/O2. 3101282| 7.925| null| S| | 4| 1| 1|Futrelle, Mrs. Ja...|female|35.0| 1| 0| 113803| 53.1| C123| S| | 5| 0| 3|Allen, Mr. Willia...| male|35.0| 0| 0| 373450| 8.05| null| S| | 6| 0| 3| Moran, Mr. James| male|null| 0| 0| 330877| 8.4583| null| Q| | 7| 0| 1|McCarthy, Mr. Tim...| male|54.0| 0| 0| 17463|51.8625| E46| S| | 8| 0| 3|Palsson, Master. ...| male| 2.0| 3| 1| 349909| 21.075| null| S| | 9| 1| 3|Johnson, Mrs. Osc...|female|27.0| 0| 2| 347742|11.1333| null| S| | 10| 1| 2|Nasser, Mrs. Nich...|female|14.0| 1| 0| 237736|30.0708| null| C| | 11| 1| 3|Sandstrom, Miss. ...|female| 4.0| 1| 1| PP 9549| 16.7| G6| S| | 12| 1| 1|Bonnell, Miss. El...|female|58.0| 0| 0| 113783| 26.55| C103| S| | 13| 0| 3|Saundercock, Mr. ...| male|20.0| 0| 0| A/5. 2151| 8.05| null| S| | 14| 0| 3|Andersson, Mr. An...| male|39.0| 1| 5| 347082| 31.275| null| S| | 15| 0| 3|Vestrom, Miss. Hu...|female|14.0| 0| 0| 350406| 7.8542| null| S| | 16| 1| 2|Hewlett, Mrs. (Ma...|female|55.0| 0| 0| 248706| 16.0| null| S| | 17| 0| 3|Rice, Master. Eugene| male| 2.0| 4| 1| 382652| 29.125| null| Q| | 18| 1| 2|Williams, Mr. Cha...| male|null| 0| 0| 244373| 13.0| null| S| | 19| 0| 3|Vander Planke, Mr...|female|31.0| 1| 0| 345763| 18.0| null| S| | 20| 1| 3|Masselmani, Mrs. ...|female|null| 0| 0| 2649| 7.225| null| C| +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+ only showing top 20 rows ``` ```python theme={null} # Note, you can also connect to Spark via Spark connect. For example: # db = SparkSQL.from_uri("sc://localhost:15002", schema=schema) spark_sql = SparkSQL(schema=schema) llm = ChatOpenAI(temperature=0) toolkit = SparkSQLToolkit(db=spark_sql, llm=llm) agent_executor = create_spark_sql_agent(llm=llm, toolkit=toolkit, verbose=True) ``` ## Example: describing a table ```python theme={null} agent_executor.run("Describe the titanic table") ``` ```output theme={null} > Entering new AgentExecutor chain... Action: list_tables_sql_db Action Input: Observation: titanic Thought:I found the titanic table. Now I need to get the schema and sample rows for the titanic table. Action: schema_sql_db Action Input: titanic Observation: CREATE TABLE langchain_example.titanic ( PassengerId INT, Survived INT, Pclass INT, Name STRING, Sex STRING, Age DOUBLE, SibSp INT, Parch INT, Ticket STRING, Fare DOUBLE, Cabin STRING, Embarked STRING) ; /* 3 rows from titanic table: PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.25 None S 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0 1 0 PC 17599 71.2833 C85 C 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.925 None S */ Thought:I now know the schema and sample rows for the titanic table. Final Answer: The titanic table has the following columns: PassengerId (INT), Survived (INT), Pclass (INT), Name (STRING), Sex (STRING), Age (DOUBLE), SibSp (INT), Parch (INT), Ticket (STRING), Fare (DOUBLE), Cabin (STRING), and Embarked (STRING). Here are some sample rows from the table: 1. PassengerId: 1, Survived: 0, Pclass: 3, Name: Braund, Mr. Owen Harris, Sex: male, Age: 22.0, SibSp: 1, Parch: 0, Ticket: A/5 21171, Fare: 7.25, Cabin: None, Embarked: S 2. PassengerId: 2, Survived: 1, Pclass: 1, Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), Sex: female, Age: 38.0, SibSp: 1, Parch: 0, Ticket: PC 17599, Fare: 71.2833, Cabin: C85, Embarked: C 3. PassengerId: 3, Survived: 1, Pclass: 3, Name: Heikkinen, Miss. Laina, Sex: female, Age: 26.0, SibSp: 0, Parch: 0, Ticket: STON/O2. 3101282, Fare: 7.925, Cabin: None, Embarked: S > Finished chain. ``` ```output theme={null} 'The titanic table has the following columns: PassengerId (INT), Survived (INT), Pclass (INT), Name (STRING), Sex (STRING), Age (DOUBLE), SibSp (INT), Parch (INT), Ticket (STRING), Fare (DOUBLE), Cabin (STRING), and Embarked (STRING). Here are some sample rows from the table: \n\n1. PassengerId: 1, Survived: 0, Pclass: 3, Name: Braund, Mr. Owen Harris, Sex: male, Age: 22.0, SibSp: 1, Parch: 0, Ticket: A/5 21171, Fare: 7.25, Cabin: None, Embarked: S\n2. PassengerId: 2, Survived: 1, Pclass: 1, Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), Sex: female, Age: 38.0, SibSp: 1, Parch: 0, Ticket: PC 17599, Fare: 71.2833, Cabin: C85, Embarked: C\n3. PassengerId: 3, Survived: 1, Pclass: 3, Name: Heikkinen, Miss. Laina, Sex: female, Age: 26.0, SibSp: 0, Parch: 0, Ticket: STON/O2. 3101282, Fare: 7.925, Cabin: None, Embarked: S' ``` ## Example: running queries ```python theme={null} agent_executor.run("whats the square root of the average age?") ``` ```output theme={null} > Entering new AgentExecutor chain... Action: list_tables_sql_db Action Input: Observation: titanic Thought:I should check the schema of the titanic table to see if there is an age column. Action: schema_sql_db Action Input: titanic Observation: CREATE TABLE langchain_example.titanic ( PassengerId INT, Survived INT, Pclass INT, Name STRING, Sex STRING, Age DOUBLE, SibSp INT, Parch INT, Ticket STRING, Fare DOUBLE, Cabin STRING, Embarked STRING) ; /* 3 rows from titanic table: PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.25 None S 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0 1 0 PC 17599 71.2833 C85 C 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.925 None S */ Thought:There is an Age column in the titanic table. I should write a query to calculate the average age and then find the square root of the result. Action: query_checker_sql_db Action Input: SELECT SQRT(AVG(Age)) as square_root_of_avg_age FROM titanic Observation: The original query seems to be correct. Here it is again: SELECT SQRT(AVG(Age)) as square_root_of_avg_age FROM titanic Thought:The query is correct, so I can execute it to find the square root of the average age. Action: query_sql_db Action Input: SELECT SQRT(AVG(Age)) as square_root_of_avg_age FROM titanic Observation: [('5.449689683556195',)] Thought:I now know the final answer Final Answer: The square root of the average age is approximately 5.45. > Finished chain. ``` ```output theme={null} 'The square root of the average age is approximately 5.45.' ``` ```python theme={null} agent_executor.run("What's the name of the oldest survived passenger?") ``` ```output theme={null} > Entering new AgentExecutor chain... Action: list_tables_sql_db Action Input: Observation: titanic Thought:I should check the schema of the titanic table to see what columns are available. Action: schema_sql_db Action Input: titanic Observation: CREATE TABLE langchain_example.titanic ( PassengerId INT, Survived INT, Pclass INT, Name STRING, Sex STRING, Age DOUBLE, SibSp INT, Parch INT, Ticket STRING, Fare DOUBLE, Cabin STRING, Embarked STRING) ; /* 3 rows from titanic table: PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.25 None S 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0 1 0 PC 17599 71.2833 C85 C 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.925 None S */ Thought:I can use the titanic table to find the oldest survived passenger. I will query the Name and Age columns, filtering by Survived and ordering by Age in descending order. Action: query_checker_sql_db Action Input: SELECT Name, Age FROM titanic WHERE Survived = 1 ORDER BY Age DESC LIMIT 1 Observation: SELECT Name, Age FROM titanic WHERE Survived = 1 ORDER BY Age DESC LIMIT 1 Thought:The query is correct. Now I will execute it to find the oldest survived passenger. Action: query_sql_db Action Input: SELECT Name, Age FROM titanic WHERE Survived = 1 ORDER BY Age DESC LIMIT 1 Observation: [('Barkworth, Mr. Algernon Henry Wilson', '80.0')] Thought:I now know the final answer. Final Answer: The oldest survived passenger is Barkworth, Mr. Algernon Henry Wilson, who was 80 years old. > Finished chain. ``` ```output theme={null} 'The oldest survived passenger is Barkworth, Mr. Algernon Henry Wilson, who was 80 years old.' ``` *** [Edit the source of this page on GitHub.](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/tools/spark_sql.mdx) [Connect these docs programmatically](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.