---
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title: Spark Connect Overview
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**Building client-side Spark applications**

In Apache Spark 3.4, Spark Connect introduced a decoupled client-server
architecture that allows remote connectivity to Spark clusters using the
DataFrame API and unresolved logical plans as the protocol. The separation
between client and server allows Spark and its open ecosystem to be
leveraged from everywhere. It can be embedded in modern data applications,
in IDEs, Notebooks and programming languages.

To get started, see [Quickstart: Spark Connect](api/python/getting_started/quickstart_connect.html).

<p style="text-align: center;">
  <img src="img/spark-connect-api.png" title="Spark Connect API" alt="Spark Connect API Diagram" />
</p>

# How Spark Connect works

The Spark Connect client library is designed to simplify Spark application
development. It is a thin API that can be embedded everywhere: in application
servers, IDEs, notebooks, and programming languages. The Spark Connect API
builds on Spark's DataFrame API using unresolved logical plans as a
language-agnostic protocol between the client and the Spark driver.

The Spark Connect client translates DataFrame operations into unresolved
logical query plans which are encoded using protocol buffers. These are sent
to the server using the gRPC framework.

The Spark Connect endpoint embedded on the Spark Server receives and
translates unresolved logical plans into Spark's logical plan operators.
This is similar to parsing a SQL query, where attributes and relations are
parsed and an initial parse plan is built. From there, the standard Spark
execution process kicks in, ensuring that Spark Connect leverages all of
Spark's optimizations and enhancements. Results are streamed back to the
client through gRPC as Apache Arrow-encoded row batches.

<p style="text-align: center;">
  <img src="img/spark-connect-communication.png" title="Spark Connect communication" alt="Spark Connect communication" />
</p>

## How Spark Connect client applications differ from classic Spark applications

One of the main design goals of Spark Connect is to enable a full separation and
isolation of the client from the server. As a consequence, there are some changes
that developers need to be aware of when using Spark Connect:

1. The client does not run in the same process as the Spark driver. This means that
   the client cannot directly access and interact with the driver JVM to manipulate
   the execution environment. In particular, in PySpark, the client does not use Py4J
   and thus the accessing the private fields holding the JVM implementation of `DataFrame`,
   `Column`, `SparkSession`, etc. is not possible (e.g. `df._jdf`).
2. By design, the Spark Connect protocol uses Sparks logical
   plans as the abstraction to be able to declaratively describe the operations to be executed
   on the server. Consequently, the Spark Connect protocol does not support all the
   execution APIs of Spark, most importantly RDDs.
3. Spark Connect provides a session-based client for its consumers. This means that the
   client does not have access to properties of the cluster that manipulate the
   environment for all connected clients. Most importantly, the client does not have access
   to the static Spark configuration or the SparkContext.

# Operational benefits of Spark Connect

With this new architecture, Spark Connect mitigates several multi-tenant
operational issues:

**Stability**: Applications that use too much memory will now only impact their
own environment as they can run in their own processes. Users can define their
own dependencies on the client and don't need to worry about potential conflicts
with the Spark driver.

**Upgradability**: The Spark driver can now seamlessly be upgraded independently
of applications, for example to benefit from performance improvements and security fixes.
This means applications can be forward-compatible, as long as the server-side RPC
definitions are designed to be backwards compatible.

**Debuggability and observability**: Spark Connect enables interactive debugging
during development directly from your favorite IDE. Similarly, applications can
be monitored using the application's framework native metrics and logging libraries.

# How to use Spark Connect

Spark Connect is available and supports PySpark and Scala
applications. We will walk through how to run an Apache Spark server with Spark
Connect and connect to it from a client application using the Spark Connect client
library.

## Download and start Spark server with Spark Connect

First, download Spark from the
[Download Apache Spark](https://spark.apache.org/downloads.html) page. Choose the
latest release in  the release drop down at the top of the page. Then choose your package type, typically
“Pre-built for Apache Hadoop 3.3 and later”, and click the link to download.

Now extract the Spark package you just downloaded on your computer, for example:

{% highlight bash %}
tar -xvf spark-{{site.SPARK_VERSION_SHORT}}-bin-hadoop3.tgz
{% endhighlight %}

In a terminal window, go to the `spark` folder in the location where you extracted
Spark before and run the `start-connect-server.sh` script to start Spark server with
Spark Connect, like in this example:

{% highlight bash %}
./sbin/start-connect-server.sh
{% endhighlight %}

Make sure to use the same version  of the package as the Spark version you
downloaded previously. In this example, Spark {{site.SPARK_VERSION_SHORT}} with Scala 2.13.

Now Spark server is running and ready to accept Spark Connect sessions from client
applications. In the next section we will walk through how to use Spark Connect
when writing client applications.

## Use Spark Connect for interactive analysis
<div class="codetabs">

<div data-lang="python" markdown="1">
When creating a Spark session, you can specify that you want to use Spark Connect
and there are a few ways to do that outlined as follows.

If you do not use one of the mechanisms outlined here, your Spark session will
work just like before, without leveraging Spark Connect.

### Set SPARK_REMOTE environment variable

If you set the `SPARK_REMOTE` environment variable on the client machine where your
Spark client application is running and create a new Spark Session as in the following
example, the session will be a Spark Connect session. With this approach, there is no
code change needed to start using Spark Connect.

In a terminal window, set the `SPARK_REMOTE` environment variable to point to the
local Spark server you started previously on your computer:

{% highlight bash %}
export SPARK_REMOTE="sc://localhost"
{% endhighlight %}

And start the Spark shell as usual:

{% highlight bash %}
./bin/pyspark
{% endhighlight %}

The PySpark shell is now connected to Spark using Spark Connect as indicated in the welcome message:

{% highlight python %}
Client connected to the Spark Connect server at localhost
{% endhighlight %}

### Specify Spark Connect when creating Spark session

You can also specify that you want to use Spark Connect explicitly when you
create a Spark session.

For example, you can launch the PySpark shell with Spark Connect as
illustrated here.

To launch the PySpark shell with Spark Connect, simply include the `remote`
parameter and specify the location of your Spark server. We are using `localhost`
in this example to connect to the local Spark server we started previously:

{% highlight bash %}
./bin/pyspark --remote "sc://localhost"
{% endhighlight %}

And you will notice that the PySpark shell welcome message tells you that
you have connected to Spark using Spark Connect:

{% highlight python %}
Client connected to the Spark Connect server at localhost
{% endhighlight %}

You can also check the Spark session type. If it includes `.connect.` you
are using Spark Connect as shown in this example:

{% highlight python %}
SparkSession available as 'spark'.
>>> type(spark)
<class 'pyspark.sql.connect.session.SparkSession'>
{% endhighlight %}

Now you can run PySpark code in the shell to see Spark Connect in action:

{% highlight python %}
>>> columns = ["id", "name"]
>>> data = [(1,"Sarah"), (2,"Maria")]
>>> df = spark.createDataFrame(data).toDF(*columns)
>>> df.show()
+---+-----+
| id| name|
+---+-----+
|  1|Sarah|
|  2|Maria|
+---+-----+
{% endhighlight %}

</div>

<div data-lang="scala"  markdown="1">
For the Scala shell, we use an Ammonite-based REPL. Otherwise, very similar with PySpark shell.

{% highlight bash %}
./bin/spark-shell --remote "sc://localhost"
{% endhighlight %}

A greeting message will appear when the REPL successfully initializes:
{% highlight bash %}
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 4.0.0-SNAPSHOT
      /_/

Type in expressions to have them evaluated.
Spark session available as 'spark'.
{% endhighlight %}

By default, the REPL will attempt to connect to a local Spark Server.
Run the following Scala code in the shell to see Spark Connect in action:

{% highlight scala %}
@ spark.range(10).count
res0: Long = 10L
{% endhighlight %}

### Configure client-server connection

By default, the REPL will attempt to connect to a local Spark Server on port 15002.
The connection, however, may be configured in several ways as described in this configuration
[reference](https://github.com/apache/spark/blob/master/connector/connect/docs/client-connection-string.md).

#### Set SPARK_REMOTE environment variable

The SPARK_REMOTE environment variable can be set on the client machine to customize the client-server
connection that is initialized at REPL startup.

{% highlight bash %}
export SPARK_REMOTE="sc://myhost.com:443/;token=ABCDEFG"
./bin/spark-shell
{% endhighlight %}

or

{% highlight bash %}
SPARK_REMOTE="sc://myhost.com:443/;token=ABCDEFG" spark-connect-repl
{% endhighlight %}

#### Configure programmatically with a connection string

The connection may also be programmatically created using _SparkSession#builder_ as in this example:

{% highlight scala %}
@ import org.apache.spark.sql.SparkSession
@ val spark = SparkSession.builder.remote("sc://localhost:443/;token=ABCDEFG").getOrCreate()
{% endhighlight %}

</div>
</div>

## Use Spark Connect in standalone applications

<div class="codetabs">


<div data-lang="python"  markdown="1">

First, install PySpark with `pip install pyspark[connect]==3.5.0` or if building a packaged PySpark application/library,
add it your setup.py file as:
{% highlight python %}
install_requires=[
'pyspark[connect]==3.5.0'
]
{% endhighlight %}

When writing your own code, include the `remote` function with a reference to
your Spark server when you create a Spark session, as in this example:

{% highlight python %}
from pyspark.sql import SparkSession
spark = SparkSession.builder.remote("sc://localhost").getOrCreate()
{% endhighlight %}


For illustration purposes, we’ll create a simple Spark Connect application, SimpleApp.py:
{% highlight python %}
"""SimpleApp.py"""
from pyspark.sql import SparkSession

logFile = "YOUR_SPARK_HOME/README.md"  # Should be some file on your system
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()
{% endhighlight %}

This program just counts the number of lines containing ‘a’ and the number containing ‘b’ in a text file.
Note that you’ll need to replace YOUR_SPARK_HOME with the location where Spark is installed.

We can run this application with the regular Python interpreter as follows:
{% highlight python %}
# Use the Python interpreter to run your application
$ python SimpleApp.py
...
Lines with a: 72, lines with b: 39
{% endhighlight %}
</div>


<div data-lang="scala"  markdown="1">
To use Spark Connect as part of a Scala application/project, we first need to include the right dependencies.
Using the `sbt` build system as an example, we add the following dependencies to the `build.sbt` file:
{% highlight sbt %}
libraryDependencies += "org.apache.spark" %% "spark-connect-client-jvm" % "3.5.0"
{% endhighlight %}

When writing your own code, include the `remote` function with a reference to
your Spark server when you create a Spark session, as in this example:

{% highlight scala %}
import org.apache.spark.sql.SparkSession
val spark = SparkSession.builder().remote("sc://localhost").getOrCreate()
{% endhighlight %}


**Note**: Operations that reference User Defined Code such as UDFs, filter, map, etc require a
[ClassFinder](https://github.com/apache/spark/blob/master/sql/connect/common/src/main/scala/org/apache/spark/sql/connect/client/ClassFinder.scala)
to be registered to pickup and upload any required classfiles. Also, any JAR dependencies must be uploaded to the server using `SparkSession#AddArtifact`.

Example:
{% highlight scala %}
import org.apache.spark.sql.connect.client.REPLClassDirMonitor
// Register a ClassFinder to monitor and upload the classfiles from the build output.
val classFinder = new REPLClassDirMonitor(<ABSOLUTE_PATH_TO_BUILD_OUTPUT_DIR>)
spark.registerClassFinder(classfinder)

// Upload JAR dependencies
spark.addArtifact(<ABSOLUTE_PATH_JAR_DEP>)
{% endhighlight %}
Here, `ABSOLUTE_PATH_TO_BUILD_OUTPUT_DIR` is the output directory where the build system writes classfiles into
and `ABSOLUTE_PATH_JAR_DEP` is the location of the JAR on the local file system.

The `REPLClassDirMonitor` is a provided implementation of `ClassFinder` that monitors a specific directory but
one may implement their own class extending `ClassFinder` for customized search and monitoring.

</div>
</div>

# Client application authentication

While Spark Connect does not have built-in authentication, it is designed to
work seamlessly with your existing authentication infrastructure. Its gRPC
HTTP/2 interface allows for the use of authenticating proxies, which makes
it possible to secure Spark Connect without having to implement authentication
logic in Spark directly.

# What is supported in Spark 3.4

**PySpark**: In Spark 3.4, Spark Connect supports most PySpark APIs, including
[DataFrame](api/python/reference/pyspark.sql/dataframe.html),
[Functions](api/python/reference/pyspark.sql/functions.html), and
[Column](api/python/reference/pyspark.sql/column.html). However,
some APIs such as [SparkContext](api/python/reference/api/pyspark.SparkContext.html)
and [RDD](api/python/reference/api/pyspark.RDD.html) are not supported.
You can check which APIs are currently
supported in the [API reference](api/python/reference/index.html) documentation.
Supported APIs are labeled "Supports Spark Connect" so you can check whether the
APIs you are using are available before migrating existing code to Spark Connect.

**Scala**: In Spark 3.5, Spark Connect supports most Scala APIs, including
[Dataset](api/scala/org/apache/spark/sql/Dataset.html),
[functions](api/scala/org/apache/spark/sql/functions$.html),
[Column](api/scala/org/apache/spark/sql/Column.html),
[Catalog](api/scala/org/apache/spark/sql/catalog/Catalog.html) and
[KeyValueGroupedDataset](api/scala/org/apache/spark/sql/KeyValueGroupedDataset.html).

User-Defined Functions (UDFs) are supported, by default for the shell and in standalone applications with
additional set-up requirements.

Majority of the Streaming API is supported, including
[DataStreamReader](api/scala/org/apache/spark/sql/streaming/DataStreamReader.html),
[DataStreamWriter](api/scala/org/apache/spark/sql/streaming/DataStreamWriter.htmll),
[StreamingQuery](api/scala/org/apache/spark/sql/streaming/StreamingQuery.html) and
[StreamingQueryListener](api/scala/org/apache/spark/sql/streaming/StreamingQueryListener.html).

APIs such as [SparkContext](api/scala/org/apache/spark/SparkContext.html)
and [RDD](api/scala/org/apache/spark/rdd/RDD.html) are unsupported in Spark Connect.

Support for more APIs is planned for upcoming Spark releases.
