Spark Dataframe
This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.
NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.
import os
os.environ["OPENAI_API_KEY"] = "...input your openai api key here..."
Spark DataFrame example
from langchain_experimental.agents.agent_toolkits import create_spark_dataframe_agent
from langchain_openai import OpenAI
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
csv_file_path = "titanic.csv"
df = spark.read.csv(csv_file_path, header=True, inferSchema=True)
df.show()
23/05/15 20:33:10 WARN Utils: Your hostname, Mikes-Mac-mini.local resolves to a loopback address: 127.0.0.1; using 192.168.68.115 instead (on interface en1)
23/05/15 20:33:10 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
23/05/15 20:33:10 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
``````output
+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+
|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
agent = create_spark_dataframe_agent(llm=OpenAI(temperature=0), df=df, verbose=True)
agent.run("how many rows are there?")
[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3mThought: I need to find out how many rows are in the dataframe
Action: python_repl_ast
Action Input: df.count()[0m
Observation: [36;1m[1;3m891[0m
Thought:[32;1m[1;3m I now know the final answer
Final Answer: There are 891 rows in the dataframe.[0m
[1m> Finished chain.[0m
'There are 891 rows in the dataframe.'
agent.run("how many people have more than 3 siblings")
[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3mThought: I need to find out how many people have more than 3 siblings
Action: python_repl_ast
Action Input: df.filter(df.SibSp > 3).count()[0m
Observation: [36;1m[1;3m30[0m
Thought:[32;1m[1;3m I now know the final answer
Final Answer: 30 people have more than 3 siblings.[0m
[1m> Finished chain.[0m
'30 people have more than 3 siblings.'
agent.run("whats the square root of the average age?")
[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3mThought: I need to get the average age first
Action: python_repl_ast
Action Input: df.agg({"Age": "mean"}).collect()[0][0][0m
Observation: [36;1m[1;3m29.69911764705882[0m
Thought:[32;1m[1;3m I now have the average age, I need to get the square root
Action: python_repl_ast
Action Input: math.sqrt(29.69911764705882)[0m
Observation: [36;1m[1;3mname 'math' is not defined[0m
Thought:[32;1m[1;3m I need to import math first
Action: python_repl_ast
Action Input: import math[0m
Observation: [36;1m[1;3m[0m
Thought:[32;1m[1;3m I now have the math library imported, I can get the square root
Action: python_repl_ast
Action Input: math.sqrt(29.69911764705882)[0m
Observation: [36;1m[1;3m5.449689683556195[0m
Thought:[32;1m[1;3m I now know the final answer
Final Answer: 5.449689683556195[0m
[1m> Finished chain.[0m
'5.449689683556195'
spark.stop()
Spark Connect example
# in apache-spark root directory. (tested here with "spark-3.4.0-bin-hadoop3 and later")
# To launch Spark with support for Spark Connect sessions, run the start-connect-server.sh script.
!./sbin/start-connect-server.sh --packages org.apache.spark:spark-connect_2.12:3.4.0
from pyspark.sql import SparkSession
# Now that the Spark server is running, we can connect to it remotely using Spark Connect. We do this by
# creating a remote Spark session on the client where our application runs. Before we can do that, we need
# to make sure to stop the existing regular Spark session because it cannot coexist with the remote
# Spark Connect session we are about to create.
SparkSession.builder.master("local[*]").getOrCreate().stop()
23/05/08 10:06:09 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
# The command we used above to launch the server configured Spark to run as localhost:15002.
# So now we can create a remote Spark session on the client using the following command.
spark = SparkSession.builder.remote("sc://localhost:15002").getOrCreate()
csv_file_path = "titanic.csv"
df = spark.read.csv(csv_file_path, header=True, inferSchema=True)
df.show()
+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+
|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
import os
from langchain_experimental.agents import create_spark_dataframe_agent
from langchain_openai import OpenAI
os.environ["OPENAI_API_KEY"] = "...input your openai api key here..."
agent = create_spark_dataframe_agent(llm=OpenAI(temperature=0), df=df, verbose=True)
agent.run(
"""
who bought the most expensive ticket?
You can find all supported function types in https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe
"""
)
[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3m
Thought: I need to find the row with the highest fare
Action: python_repl_ast
Action Input: df.sort(df.Fare.desc()).first()[0m
Observation: [36;1m[1;3mRow(PassengerId=259, Survived=1, Pclass=1, Name='Ward, Miss. Anna', Sex='female', Age=35.0, SibSp=0, Parch=0, Ticket='PC 17755', Fare=512.3292, Cabin=None, Embarked='C')[0m
Thought:[32;1m[1;3m I now know the name of the person who bought the most expensive ticket
Final Answer: Miss. Anna Ward[0m
[1m> Finished chain.[0m
'Miss. Anna Ward'
spark.stop()