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What is Broadcast Join in Spark and how does it work? Broadcast join is an optimization technique in the Spark SQL engine that is used to join two DataFrames. This technique is ideal for joining a large DataFrame with a smaller one. Traditional joins take longer as they require more data shuffling and data is always collected at the driver.

In this article, I will explain what is Broadcast Join, its application, and analyze its physical plan.

1. Spark Broadcast Join

Spark Broadcast Join
Spark Broadcast Join

As you know Spark splits the data into different nodes for parallel processing, when you have two DataFrames, the data from both are distributed across multiple nodes in the cluster so, when you perform traditional join, Spark is required to shuffle the data. Shuffle is needed as the data for each joining key may not colocate on the same node and to perform join the data for each key should be brought together on the same node. Hence, the traditional join is a very expensive operation in Spark.

Spark Broadcast Join is an important part of the Spark SQL execution engine, With broadcast join, Spark broadcast the smaller DataFrame to all executors and the executor keeps this DataFrame in memory and the larger DataFrame is split and distributed across all executors so that Spark can perform a join without shuffling any data from the larger DataFrame as the data required for join colocated on every executor.

Note: In order to use Broadcast Join, the smaller DataFrame should be able to fit in Spark Drivers and Executors memory. If the DataFrame can’t fit in memory you will be getting out-of-memory errors. You can also increase the size of the broadcast join threshold using some properties which I will be discussing later.

2. Types of Broadcast join.

There are two types of broadcast joins.

  • Broadcast hash joins: In this case, the driver builds the in-memory hash DataFrame to distribute it to the executors.
  • Broadcast nested loop join: It is a nested for-loop join. It is very good for non-equi joins or coalescing joins.

3. Configuring Spark Auto Broadcast join.

We can provide the max size of DataFrame as a threshold for automatic broadcast join detection in Spark. This can be set up by using autoBroadcastJoinThreshold configuration in Spark SQL conf. Its value purely depends on the executor’s memory.

//Enable broadcast Join and 
//Set Threshold limit of size in bytes of a dataFrame to broadcast
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 104857600)

//Disable broadcast Join.
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", -1)

The threshold value for broadcast DataFrame is passed in bytes and can also be disabled by setting up its value as -1.

4. Example of a Broadcast Join

For our demo purpose, let us create two DataFrames of one large and one small using Databricks. Here we are creating the larger DataFrame from the dataset available in Databricks and a smaller one manually.

//Create a Larger DataFrame using weather Dataset in Databricks
val largeDF =

//Create a smaller dataFrame with abbreviation of codes
val smallerDF = Seq(("C", "Celcius"),
                    ("F", "Fahrenheit")
                   ).toDF("code", "realUnit")

Now let’s broadcast the smallerDF and join it with largerDF and see the result.

// Perform broadast join
import org.apache.spark.sql.functions.broadcast
  smallerDF("code")  largeDf("UNIT")


Spark Broadcast Join
Spark Broadcast Join

5. Analyze Broadcast Join

We can use the EXPLAIN() method to analyze how the Spark broadcast join is physically implemented in the backend.

// Explain broadcast join
import org.apache.spark.sql.functions.broadcast
  smallerDF("code")  largeDF("UNIT")

The parameter “extended=false” to the EXPLAIN() method results in the physical plan that gets executed on the Spark executors.


== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- BroadcastHashJoin [coalesce(UNIT#543, ), isnull(UNIT#543)], [coalesce(code#560, ), isnull(code#560)], Inner, BuildRight, false
   :- GlobalLimit 2000
   :  +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#313]
   :     +- LocalLimit 2000
   :        +- FileScan parquet [NAME#537,STATION#538,LATITUDE#539,LONGITUDE#540,ELEVATION#541,DATE#542,UNIT#543,TAVG#544] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex(1 paths)[dbfs:/mnt/training/weather/StationData/stationData.parquet], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<NAME:string,STATION:string,LATITUDE:float,LONGITUDE:float,ELEVATION:float,DATE:date,UNIT:s...
   +- BroadcastExchange HashedRelationBroadcastMode(ArrayBuffer(coalesce(input[0, string, true], ), isnull(input[0, string, true])),false), [id=#316]
      +- LocalTableScan [code#560, realUnit#561]

Notice how the physical plan is created by the Spark in the above example.

  • First, It read the parquet file and created a Larger DataFrame with limited records.
  • Then, BroadcastHashJoin is performed between the smallerDF and LargerDF using the condition provided.
  • Even if the smallerDF is not specified to be broadcasted in our code, Spark automatically broadcasts the smaller DataFrame into executor memory by default.

6. Conclusion

  • Spark Broadcast joins cannot be used when joining two large DataFrames.
  • Broadcast join naturally handles data skewness as there is very minimal shuffling.
  • The limitation of broadcast join is that we have to make sure the size of the smaller DataFrame gets fits into the executor memory.

Naveen Nelamali

Naveen Nelamali (NNK) is a Data Engineer with 20+ years of experience in transforming data into actionable insights. Over the years, He has honed his expertise in designing, implementing, and maintaining data pipelines with frameworks like Apache Spark, PySpark, Pandas, R, Hive and Machine Learning. Naveen journey in the field of data engineering has been a continuous learning, innovation, and a strong commitment to data integrity. In this blog, he shares his experiences with the data as he come across. Follow Naveen @ LinkedIn and Medium