Visualizing Hash Match Join Internals And Understanding Their Implications

This post is part 3 in a series about physical join operators (be sure to check out part 1 - nested loops joins, and part 2 - merge joins).

Watch this week's video on YouTube

Hash Match joins are the dependable workhorses of physical join operators.

While Nested Loops joins will fail if the data is too large to fit into memory, and Merge Joins require that the input data are sorted, a Hash Match will join any two data inputs you throw at it (as long as the join has an equality predicate and you have enough space in tempdb). 

The base hash match algorithm has two phases that work like this:


During the first "Build" phase, SQL Server builds an in-memory hash table from one of the inputs (typically the smaller of the two).  The hashes are calculated based on the join keys of the input data and then stored along with the row in the hash table under that hash bucket.  Most of the time there is only 1 row of data per hash bucket except when:

  1. There are rows with duplicate join keys.
  2. The hashing function produces a collision and totally different join keys receive the same hash (uncommon but possible).

Once the hash table is built, SQL Server begins the "Probe" phase.  During this second phase, SQL Server calculates the join key hash for each row in the second input, and checks to see if it exists in the hash table created in the first build phase.  If it finds a match for that hash, it then verifies if the join keys between the row(s) in the hash table and the row from the second table actually match (it needs to perform this verification due to potential hash collisions).

A common variation on this hash match algorithm occurs when the build phase cannot create a hash table that can be fully stored in memory:


This happens when the data is larger than what can be stored in memory or when SQL Server grants an inadequate amount of memory required for the hash match join.

When SQL Server runs doesn't have enough memory to store the build phase hash table, it proceeds by keeping 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 buckets from the build phase that are in memory. If the bucket that the row potentially matches isn't 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. It then compares the second input's rows (currently residing in tempdb) with the new in-memory buckets.

As with every physical join operator in this series, there are way more details about the hash match operator on Hugo Kornelis's reference on hash matches.

What Do Hash Match Joins Reveal?

Knowing the internals of how a hash match join works allows us to infer what the optimizer thinks about our data and the join's upstream operators, helping us focus our performance tuning efforts. 

Here are a few scenarios to consider the next time you see a hash match join being used in your execution plan:

  • While hash match joins are able to join huge sets of data, building the hash table from the first input is a blocking operation that will prevent downstream operators from executing. Due to this, I always check to see if there is an easy way to convert a hash match to either a nested loops or merge join.  Sometimes that won't be possible (too many rows for nested loops or unsorted data for merge joins) but it's always worth checking if a simple index change or improved estimates from a statistics update would cause SQL Server to pick a non-blocking hash match join operator.

  • Hash match joins are great for large joins - since they can spill to tempdb, it allows them to perform joins on large datasets that would fail an in-memory join with either the nested loops or merge join operators.

    • Seeing a hash match join operator means SQL Server thinks the upstream inputs are big.  If we know our inputs shouldn't be that big, then it's worth checking if we have a stats/estimation problem that is causing SQL Server to choose a hash match join incorrectly.
  • When executed in memory, hash match joins are fairly efficient. Problems arise when the build phase spills to tempdb.

    • If I notice the little yellow triangle indicating that the join is spilling to tempdb, I take a look to see why: if the data is larger than the server's available memory, there's not much that can be done there, but if the memory grant seems unusually small that means we probably have another statistics problem that is providing the SQL Server optimizer estimates that are too low.

Visualizing Merge Join Internals And Understanding Their Implications

This post is part 2 in a series about physical join operators (be sure to check out part 1 - nested loops joins, and part 3 - hash match joins).

Watch this week's video on YouTube

Merge joins are theoretically the fastest* physical join operators available, however they require that data from both inputs is sorted:


The base algorithm works as follows: SQL Server compares the first rows from both sorted inputs.  It then continues comparing the next rows from the second input as long as the values match the first input's value.

Once the values no longer match, SQL Server increments the row of whichever input has the smaller value - it then continues performing comparisons and outputting any joined records. (For more detailed information, be sure to check out Craig Freedman's post on merge joins.)

This is efficient because in most instances SQL Server never has to go back and read any rows multiple times.  The exception here happens when duplicate values exist in both input tables (or rather, SQL Server doesn't have meta data available proving that duplicates don't exist in both tables) and SQL Server has to perform a many-to-many merge join:


Note: The image above and the explanation below are "good enough" for understanding this process for practical purposes - if you want to dive into the peek-ahead buffers, optimizations, and other inner workings of this process, I highly recommend reading through Hugo Kornelis's reference on merge joins.

A many-to-many join forces SQL Server to write any duplicated values in the second table into a worktable in tempdb and do the comparisons there.  If those duplicated values are also duplicated in the first table, SQL Server then compares the first table's values to those already stored in the worktable.

What Do Merge Joins Reveal?

Knowing the internals of how a merge join works allows us to infer what the optimizer thinks about our data and the join's upstream operators, helping us focus our performance tuning efforts. 

Here are a few scenarios to consider the next time you see a merge join being used in your execution plan:

  • The optimizer chooses to use a merge join when the input data is already sorted or SQL Server can sort the data for a low enough cost.  Additionally, the optimizer is fairly pessimistic at calculating the costs of merge joins (great explanation by Joe Obbish), so if a merge join makes its way into your plans, it probably means that it is fairly efficient.

  • While a merge join may be efficient, it's always worth looking at why the data coming in to the merge join operator is already sorted:

    • If it's sorted because the merge join is pulling data directly from an index sorted on your join keys, then there is not much to be concerned about.
    • If the optimizer added a sort to the upstream merge join though, it may be worth investigating whether it's possible to presort that data so SQL Server doesn't need to sort it on its own.  Often times this can be as simple as redefining an included index column to a key column - if you are adding it as the last key column in the index then regression impact is usually minor but you may be able to allow SQL Server to use the merge join without any additional sorting required.
  • If your inputs contain many duplicates, it may be worth checking if a merge join is really the most efficient operator for the join.  As outlined above, many-to-many merge joins require tempdb usage which could become a bottle neck!

So while merge joins are typically not the high-cost problem spots in your execution plans, it's always worth investigating upstream operators to see if some additional improvements can be made.


*NOTE: There are always exceptions to the rule.  Merge joins have the fastest algorithm since each row only needs to be read once from the source inputs.  Also, optimizations occurring in other join operators can give those operators better performance under certain conditions.

For example, a single row outer table with an indexed inner table using a nested loops join will outperform the same setup with a merge join because of the inner loops joins' optimizations:

CREATE TABLE T1 (Id int identity PRIMARY KEY, Col1 CHAR(1000));


CREATE TABLE T2 (Id int identity PRIMARY KEY, Col1 CHAR(1000));

GO 100

-- Turn on execution plans and check actual rows for T2


*There might also be instances where inputs with many duplicate records that require worktables may be slower than a nested loop join. *

As I mentioned though, I typically find these types of scenarios to be the exceptions when encountering merge joins in the real-world.