Welcome to another blog post in our series on SQL Server concepts and ideas. In this post, we will be discussing the powerful and versatile Hash Match join operator.

Hash Match joins are known for their ability to join any two data inputs, as long as the join has an equality predicate and there is enough space in tempdb. Unlike Nested Loops joins, which can fail if the data is too large to fit into memory, and Merge Joins, which require sorted input data, Hash Match joins are the dependable workhorses of physical join operators.

The base hash match algorithm consists of two phases. In the first “Build” phase, SQL Server builds an in-memory hash table from one of the inputs, typically the smaller of the two. The hash values are calculated based on the join keys of the input data and stored along with the corresponding rows in the hash table. In most cases, there is only one row of data per hash bucket, except in scenarios where there are rows with duplicate join keys or when the hashing function produces a collision and different join keys receive the same hash.

Once the hash table is built, SQL Server proceeds to the second “Probe” phase. During this phase, SQL Server calculates the join key hash for each row in the second input and checks if it exists in the hash table created in the build phase. If a match is found, SQL Server verifies if the join keys between the rows in the hash table and the row from the second table actually match, taking into account potential hash collisions.

In cases where the build phase cannot create a hash table that can be fully stored in memory, SQL Server employs a variation of the hash match algorithm. It keeps some of the buckets in memory while spilling the other buckets to tempdb. During the probe phase, SQL Server joins the rows of data from the second input to the in-memory buckets from the build phase. If a bucket that a row potentially matches is not currently in memory, SQL Server writes that row to tempdb for later comparison. Once the matches for one bucket are complete, SQL Server clears that data from memory and loads the next bucket(s) into memory.

Understanding the internals of how a hash match join works can provide valuable insights into the optimizer’s thought process and help guide performance tuning efforts. Here are a few scenarios to consider when encountering a hash match join in your execution plan:

• Consider converting a hash match join to either a nested loops or merge join if possible. Hash match joins can be blocking operations that prevent downstream operators from executing.
• Hash match joins are ideal for large joins, as they can spill to tempdb and perform joins on large datasets that would fail with in-memory join operators like nested loops or merge join.
• If a hash match join is present, it indicates that SQL Server believes the upstream inputs are big. If this contradicts your expectations, it may be worth investigating potential statistics or estimation issues.
• Hash match joins are efficient when executed in memory, but problems can arise when the build phase spills to tempdb. If you notice the join spilling to tempdb, investigate the reasons behind it, such as insufficient memory grants or unusually large data.

By understanding the behavior and characteristics of hash match joins, you can gain valuable insights into your SQL Server queries and optimize their performance accordingly.

Stay tuned for more informative blog posts on SQL Server concepts and ideas!