Creating reproducible Apache Spark examples is essential for debugging, sharing, and understanding Spark applications. Here are some best practices and detailed steps to ensure your Spark job is reproducible:
1. Use a Fixed Seed for Random Generators
When your application involves any randomness, ensure you set a fixed seed for all random number generators. This is crucial for making sure that the results are the same each time you run the job.
Example in PySpark:
from pyspark.sql import SparkSession
from pyspark.sql.functions import rand
spark = SparkSession.builder.appName("ReproducibleExample").getOrCreate()
df = spark.range(10).withColumn("random", rand(seed=42))
df.show()
+---+-------------------+
| id| random|
+---+-------------------+
| 0| 0.3745401188473625|
| 1| 0.9507143064099162|
| 2| 0.7319939418114051|
| 3| 0.5986584841970366|
| 4| 0.1560186404424365|
| 5| 0.1559945203362026|
| 6|0.05808361216819946|
| 7|0.8661761457749352|
| 8|0.6011150117432088|
| 9|0.7080725777960455|
+---+-------------------+
2. Set Environment Variables
Ensure that your Spark environment settings are consistent. This includes Spark configurations, environment variables, and PySpark variables. It is a good idea to explicitly set these at the start of your application.
Example in PySpark:
spark.conf.set("spark.sql.shuffle.partitions", "2")
spark.conf.set("spark.executor.memory", "1g")
# More configurations as needed
3. Fix the Ordering of DataFrames
By default, operations that output DataFrames such as groupBy and join may not guarantee a specific order. Always perform explicit sorting if the order is essential for reproducibility.
Example in Scala:
val spark = SparkSession.builder.appName("ReproducibleExample").getOrCreate()
import spark.implicits._
val df = Seq((1, "Alice"), (2, "Bob"), (3, "Cathy")).toDF("id", "name")
val sortedDf = df.orderBy("id")
sortedDf.show()
+---+-----+
| id| name|
+---+-----+
| 1|Alice|
| 2| Bob|
| 3|Cathy|
+---+-----+
4. Use Deterministic Operations
Avoid operations that can cause non-deterministic results. For example, avoid using methods like mapPartitions if the code inside the partition is not deterministic.
5. Fix the Version of Spark and Dependencies
Ensure that you are using the same version of Spark and libraries across your environments. Even minor version changes can result in different behavior.
Example in Maven for Java/Scala:
“`xml
“`
6. Capture the Input Data
Use fixed input data or snapshot your input data in a versioned storage like HDFS, S3, or a local filesystem. This ensures the data remains consistent across different runs.
Example in PySpark:
fixedInputPath = "s3a://your-bucket/path/to/fixed/data"
df = spark.read.csv(fixedInputPath, header=True)
Summary
Adhering to these best practices will help you to create reproducible examples in Apache Spark. Fixed seeds for random functions, consistent environment settings, deterministic operations, fixed versions for dependencies, and controlled input data are crucial steps in achieving reproducibility.