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--- spark:using-python [2022/12/14 12:59]
straka [Usage Examples]
+++ spark:using-python [2026/10/14 19:56] (current)
straka Drop the ipython3 override.
@@ Line 8: / Line 8: @@
 <file>pyspark</file>
-Better interactive shell with code completion using ''ipython3'' (do `pip3 install --user ipython` if you do not have it) can be started using:
-<file>PYSPARK_DRIVER_PYTHON=ipython3 pyspark</file>
 As described in [[running-spark-on-single-machine-or-on-cluster|Running Spark on Single Machine or on Cluster]], environmental variable ''MASTER'' specifies which Spark master to use (or whether to start a local one).
@@ Line 16: / Line 14: @@
 Consider the following simple script computing 10 most frequent words of Czech Wikipedia:
 <file python>
-(sc.textFile("/lnet/troja/data/npfl118/wiki/cs/wiki.txt", 3*sc.defaultParallelism)
+(sc.textFile("/net/projects/spark-example-data/wiki-cs", 3*sc.defaultParallelism)
    .flatMap(lambda line: line.split())
    .map(lambda word: (word, 1))
-   .reduceByKey(lambda c1,c2: c1+c2)
+   .reduceByKey(lambda c1, c2: c1+c2)
    .sortBy(lambda word_count: word_count[1], ascending=False)
    .take(10))
@@ Line 25: / Line 23: @@
   * run interactive shell using existing Spark cluster (i.e., inside ''spark-srun''), or start local Spark cluster using as many threads as there are cores if there is none:
-  <file>PYSPARK_DRIVER_PYTHON=ipython3 pyspark</file>
+  <file>pyspark</file>
   * run interactive shell with local Spark cluster using one thread:
-  <file>MASTER=local PYSPARK_DRIVER_PYTHON=ipython3 pyspark</file>
+  <file>MASTER=local pyspark</file>
-  * start Spark cluster (10 machines, 1GB RAM each) on SGE and run interactive shell:
+  * start Spark cluster (10 machines, 2GB RAM each) on Slurm and run interactive shell:
-  <file>PYSPARK_DRIVER_PYTHON=ipython3 spark-qrsh 10 1G pyspark</file>
+  <file>spark-srun 10 2G pyspark</file>
-Note that ''PYSPARK_DRIVER_PYTHON'' variable can be left out or specified in ''.bashrc'' (or other configuration files).
@@ Line 59: / Line 55: @@
    .flatMap(lambda line: line.split())
    .map(lambda token: (token, 1))
-   .reduceByKey(lambda x,y: x + y)
+   .reduceByKey(lambda x, y: x + y)
    .sortBy(lambda word_count: word_count[1], ascending=False)
    .saveAsTextFile(output))
@@ Line 65: / Line 61: @@
 </file>
-  * run ''word_count.py'' script inside existing Spark cluster (i.e., inside ''spark-qsub'' or ''spark-qrsh''), or start local Spark cluster using as many threads as there are cores if there is none:
+  * run ''word_count.py'' script inside existing Spark cluster (i.e., inside ''spark-sbatch'' or ''spark-srun''), or start local Spark cluster using as many threads as there are cores if there is none:
   <file>spark-submit word_count.py /net/projects/spark-example-data/wiki-cs outdir</file>
   * run ''word_count.py'' script with local Spark cluster using one thread:
   <file>MASTER=local spark-submit word_count.py /net/projects/spark-example-data/wiki-cs outdir</file>
-  * start Spark cluster (10 machines, 1GB RAM each) on SGE and run ''word_count.py'' script:
+  * start Spark cluster (10 machines, @GB RAM each) using Slurm and run ''word_count.py'' script:
-  <file>spark-qsub 10 1G spark-submit word_count.py /net/projects/spark-example-data/wiki-cs outdir</file>
+  <file>spark-sbatch 10 2G spark-submit word_count.py /net/projects/spark-example-data/wiki-cs outdir</file>
 ===== Using Virtual Environments =====

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