# Access the Spark shell
<a name="emr-spark-shell"></a>

The Spark shell is based on the Scala REPL (Read-Eval-Print-Loop). It allows you to create Spark programs interactively and submit work to the framework. You can access the Spark shell by connecting to the primary node with SSH and invoking `spark-shell`. For more information about connecting to the primary node, see [Connect to the primary node using SSH](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-connect-master-node-ssh.html) in the *Amazon EMR Management Guide*. The following examples use Apache HTTP Server access logs stored in Amazon S3.

**Note**  
The bucket in these examples is available to clients that can access US East (N. Virginia).

 By default, the Spark shell creates its own [SparkContext](https://spark.apache.org/docs/latest/api/scala/org/apache/spark/SparkContext.html) object called `sc`. You can use this context if it is required within the REPL. sqlContext is also available in the shell and it is a [HiveContext](https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.hive.HiveContext). 

**Example Use the Spark shell to count the occurrences of a string in a file stored in Amazon S3**  
This example uses `sc` to read a text file that's stored in Amazon S3.  

```
scala> sc
res0: org.apache.spark.SparkContext = org.apache.spark.SparkContext@404721db

scala> val textFile = sc.textFile("s3://elasticmapreduce/samples/hive-ads/tables/impressions/dt=2009-04-13-08-05/ec2-0-51-75-39.amazon.com.rproxy.govskope.ca-2009-04-13-08-05.log")
```
Spark creates the textFile and associated [data structure](https://spark.apache.org/docs/latest/programming-guide.html#resilient-distributed-datasets-rdds). Next, the example counts the number of lines in the log file with the string "cartoonnetwork.com":  

```
scala> val linesWithCartoonNetwork = textFile.filter(line => line.contains("cartoonnetwork.com")).count()
linesWithCartoonNetwork: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[2] at filter at <console>:23
<snip>
<Spark program runs>
scala> linesWithCartoonNetwork
res2: Long = 9
```

**Example Use the Python-based Spark shell to count the occurrences of a string in a file stored in Amazon S3**  
Spark also includes a Python-based shell, `pyspark`, that you can use to prototype Spark programs written in Python. Just as with `spark-shell`, invoke `pyspark` on the primary node; it also has the same [SparkContext](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.SparkContext.html#pyspark.SparkContext) object.   

```
>>> sc
<pyspark.context.SparkContext object at 0x7fe7e659fa50>
>>> textfile = sc.textFile("s3://elasticmapreduce/samples/hive-ads/tables/impressions/dt=2009-04-13-08-05/ec2-0-51-75-39.amazon.com.rproxy.govskope.ca-2009-04-13-08-05.log")
```
Spark creates the textFile and associated [data structure](https://spark.apache.org/docs/latest/programming-guide.html#resilient-distributed-datasets-rdds). Next, the example counts the number of lines in the log file with the string "cartoonnetwork.com".  

```
>>> linesWithCartoonNetwork = textfile.filter(lambda line: "cartoonnetwork.com" in line).count()
15/06/04 17:12:22 INFO lzo.GPLNativeCodeLoader: Loaded native gpl library from the embedded binaries
15/06/04 17:12:22 INFO lzo.LzoCodec: Successfully loaded & initialized native-lzo library [hadoop-lzo rev EXAMPLE]
15/06/04 17:12:23 INFO fs.EmrFileSystem: Consistency disabled, using com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem as filesystem implementation
<snip>
<Spark program continues>
>>> linesWithCartoonNetwork
9
```