The Broadcast Hash Join is a join optimization strategy used in distributed data processing frameworks like Apache Spark, Dask, and others. It’s particularly effective when one of the tables being joined is significantly smaller than the other and can fit into the memory of each executor node in the cluster.

Here’s how it works:

Algorithm:

1. Broadcast Phase: The smaller table (often called the “broadcast table” or “dimension table”) is serialized and copied to all the executor nodes in the cluster. This happens only once.
2. Hash Join Phase: On each executor node, a hash table is built in memory using the join keys from the broadcasted smaller table. Then, the executor processes its partition of the larger table (often called the “fact table”). For each row in the larger table’s partition, the join key is used to probe the in-memory hash table of the smaller table. Matching rows are then joined.

When is Broadcast Hash Join Used?

• Equi-Joins: It primarily works for equi-joins (joins based on equality of columns).
• Small vs. Large Tables: It’s most efficient when one table is small enough to be held in the memory of each executor. The framework often has a configuration parameter (spark.sql.autoBroadcastJoinThreshold in Spark, default is often around 10MB) to automatically decide when to use this strategy based on table size statistics. You can also explicitly hint to the system to use a broadcast join.
• Star Schema: It’s very effective in star schema data warehouses where a large fact table is joined with several smaller dimension tables.

Advantages of Broadcast Hash Join:

• Eliminates Shuffling for the Smaller Table: The primary advantage is that the smaller table is broadcasted only once. This avoids the expensive shuffle operation across the network that is required in other join strategies like the Shuffle Hash Join or Sort-Merge Join for the smaller table. Network I/O is often a bottleneck in distributed systems.
• Faster Join Performance: By having the smaller table readily available in memory on each executor, the join operation becomes a local in-memory lookup (hash table probe), which is significantly faster than reading data from disk or shuffling it across the network.
• Reduced Latency: The overall query execution time can be reduced due to the elimination of shuffling for the smaller table.

Disadvantages of Broadcast Hash Join:

• Memory Overhead: The smaller table must fit into the memory of each executor node. If the table being broadcasted is too large, it can lead to excessive memory pressure, Out-Of-Memory (OOM) errors, and performance degradation as the system might start spilling data to disk.
• Network Transfer of the Broadcast Table: While shuffling is avoided for the smaller table during the join, there is an initial cost of transferring the broadcast table to all executors. For very large broadcast tables or clusters with a large number of executors, this initial transfer can take time.
• Not Suitable for Very Large Tables on Both Sides: If both tables are large and cannot fit into the memory of the executors, broadcast hash join is not a viable option.
• Potential for Redundant Data: The smaller table is replicated on every executor, which might lead to increased overall memory usage across the cluster.

In the context of Parquet:

When performing a broadcast hash join with tables stored in Parquet format, the benefits of Parquet (columnar storage, efficient compression, predicate pushdown) still apply when reading the larger table. Only the necessary join key columns and filter columns from the larger Parquet table will be read, further optimizing the join process. The smaller Parquet table will be read and then broadcasted in its entirety (or the necessary columns for the join).

In summary, Broadcast Hash Join is a powerful optimization technique for joins in distributed data processing when one of the tables is small enough to fit in memory. It significantly improves performance by avoiding data shuffling for the smaller table and performing local in-memory joins on each executor.