Spark Connect Overview: Building Client-Side Spark Applications

Spark Connect Overview: Building Client-Side Spark Applications

Apache Spark 3.4 introduced Spark Connect, a revolutionary way to build client-side Spark applications using a decoupled client-server architecture. This innovation allows users to connect remotely to Spark clusters with ease, leveraging the full power of Spark’s DataFrame API and unresolved logical plans.

What is Spark Connect?

Spark Connect enables remote connectivity to Spark clusters using a simple API, which translates client-side DataFrame operations into unresolved logical query plans. These plans are then sent to the Spark server using gRPC, ensuring seamless communication between the client and the Spark driver. The server processes the plans and sends the results back, encoded in Apache Arrow, optimizing data transfer.

By separating the client from the server, Spark Connect enhances the flexibility of Spark applications, allowing them to be embedded into modern data applications, IDEs, notebooks, and a variety of programming languages. It is especially useful for distributed data processing and analytics.

The Architecture Behind Spark Connect

The Spark Connect client library provides a thin API that simplifies the development of Spark applications. The client sends operations to the server in the form of unresolved logical query plans, which are encoded using protocol buffers and transported over gRPC. Once received by the Spark server, these plans are parsed and processed just like SQL queries, enabling the use of Spark’s powerful optimization engine.

The Spark Connect server, once it receives these requests, translates them into logical plan operators and initiates the usual Spark execution pipeline, ensuring that Spark's optimizations are applied. The results are streamed back to the client in the form of Arrow-encoded row batches.

Getting Started with Spark Connect

To begin using Spark Connect, follow these steps:

1. Download Spark: Download Apache Spark 3.4 or newer from the Apache Spark website. After downloading, extract the Spark package:

   tar -xvf spark-3.5.4-bin-hadoop3.tgz
   

2. Start the Spark Server: To enable Spark Connect, you’ll need to start the Spark server with the Spark Connect package:

   ./sbin/start-connect-server.sh --packages org.apache.spark:spark-connect_2.12:3.5.4
   

   Make sure to match the Spark Connect package version with the version of Spark you’ve downloaded.

Using Spark Connect for Interactive Analysis

Once your Spark server is up and running, you can connect to it using Spark Connect for interactive analysis. There are several ways to specify that your Spark session should use Spark Connect.

1. Set the SPARK_REMOTE Environment Variable

You can set the SPARK_REMOTE environment variable to point to the Spark server running locally:

export SPARK_REMOTE="sc://localhost"
./bin/pyspark

The PySpark shell will then connect to Spark via Spark Connect. You’ll see a message indicating that the connection has been established:

Client connected to the Spark Connect server at localhost

2. Specify Spark Connect in the Spark Session

Alternatively, you can specify the --remote option when starting the PySpark shell:

./bin/pyspark --remote "sc://localhost"

This will also connect your session to the Spark Connect server at localhost.

You can confirm the session is using Spark Connect by checking the session type:

>>> type(spark)
<class 'pyspark.sql.connect.session.SparkSession'>

Running Spark Code

You can now interact with Spark via the DataFrame API. Here’s a quick example of running Spark operations:

columns = ["id", "name"]
data = [(1, "Sarah"), (2, "Maria")]
df = spark.createDataFrame(data).toDF(*columns)
df.show()

This will display:

+---+-----+
| id| name|
+---+-----+
|  1|Sarah|
|  2|Maria|
+---+-----+

Using Spark Connect in Standalone Applications

To use Spark Connect in standalone applications, you need to install PySpark with the connect option:

pip install pyspark[connect]==3.5.0

In your application code, create a Spark session and specify the remote server:

from pyspark.sql import SparkSession
spark = SparkSession.builder.remote("sc://localhost").getOrCreate()

Here’s a simple Python application that uses Spark Connect:

from pyspark.sql import SparkSession

logFile = "YOUR_SPARK_HOME/README.md"  # Replace with actual path
spark = SparkSession.builder.remote("sc://localhost").appName("SimpleApp").getOrCreate()
logData = spark.read.text(logFile).cache()

numAs = logData.filter(logData.value.contains('a')).count()
numBs = logData.filter(logData.value.contains('b')).count()

print("Lines with a: %i, lines with b: %i" % (numAs, numBs))

spark.stop()

Run this application using the Python interpreter:

python SimpleApp.py

This simple app counts the occurrences of 'a' and 'b' in the specified text file.

Authentication with Spark Connect

While Spark Connect does not include built-in authentication, it supports integration with existing authentication mechanisms. Since it uses the gRPC HTTP/2 interface, you can secure your connection using authenticating proxies without having to implement authentication logic directly in Spark.

Conclusion

Spark Connect opens up a new world of possibilities for building client-side Spark applications. By decoupling the client from the server, Spark Connect enables remote access, making it easier to integrate Spark with modern data applications, IDEs, notebooks, and various programming languages. It simplifies Spark development, enhances flexibility, and maintains all of Spark’s optimizations, making it a powerful tool for interactive analysis and distributed data processing. Whether you are building applications in Python, Scala, or other languages, Spark Connect allows you to tap into the full potential of Apache Spark from anywhere.