MySQL 5.7: Improved JOIN Order by Taking Condition Filter Effect into Account

One of the major challenges of query optimizers is to correctly estimate how many rows qualify from each table of a join. If the estimates are wrong, the optimizer may choose a non-optimal join order.

Before MySQL 5.7, the estimated number of rows from a table only took into account the conditions from the WHERE clause that were used to set up the access method (e.g., the size of an index range scan). This often led to row estimates that were far too high, resulting in very wrong cost estimates for join plans. To improve this issue, MySQL 5.7 introduced a cost model that considered the entire WHERE condition when estimating the number of qualifying rows from each table. This model estimates the filtering effect of the table’s conditions.

As shown in the above figure, the condition filter effect will reduce the estimated number of rows from t_x that will lead to an index look-up on t_x+1. For more details on how condition filtering works, see two earlier blog posts on this topic: part1, part2.

Taking condition filtering into account, the join optimizer will in many cases be able to find a more optimal join order. However, there are cases where the optimizer will overestimate the filtering effect and choose a non-optimal query plan. In this blog post, I will show an example of a query that benefits from condition filtering, and in a follow-up blog post I will discuss what could be done if condition filtering does not have the desired effect.

Example: DBT-3 Query 8

To show the benefits of condition filtering, we will look at Query 8 in the DBT-3 benchmark:

SELECT o_year,
       SUM(CASE WHEN nation = 'FRANCE' THEN volume ELSE 0 END) / SUM(volume) AS mkt_share
FROM (
    SELECT EXTRACT(YEAR FROM o_orderdate) AS o_year,
           l_extendedprice * (1 - l_discount) AS volume, n2.n_name AS nation
    FROM part
    JOIN lineitem ON p_partkey = l_partkey
    JOIN supplier ON s_suppkey = l_suppkey
    JOIN orders ON l_orderkey = o_orderkey
    JOIN customer ON o_custkey = c_custkey
    JOIN nation n1 ON c_nationkey = n1.n_nationkey
    JOIN region ON  n1.n_regionkey = r_regionkey   
    JOIN nation n2 ON s_nationkey = n2.n_nationkey
    WHERE r_name = 'EUROPE' AND o_orderdate BETWEEN '1995-01-01' AND '1996-12-31'
      AND p_type = 'PROMO BRUSHED STEEL'
) AS all_nations GROUP BY o_year ORDER BY o_year;

Query 8 is called National Market Share Query, and it finds the market share in Europe for French suppliers of a given part type. You do not need to understand this query in detail. The main point is that 8 tables are joined, and that it is important to find an efficient join order for the query to perform well.

In MySQL 5.6, Visual EXPLAIN shows the following query plan for Query 8:

Tables are joined from left-to-right, starting with a full table scan of the region table, doing index look-ups into all other tables, with the part table as the last table. The execution time for this query is around 6 seconds on a scale factor 1 DBT-3 database.

If we look at the WHERE clause of the query, we see that there is one condition with high selectivity: We are interested in just one particular of many possible part types. That the region should be Europe, and that the time period is restricted to two years, will still leave many candidate rows, so these conditions have low selectivity. In other words, a query plan that put the part table at the end is not optimal. If part was processed early, we would be able to filter out many rows, and the number of index look-ups into other tables could be significantly reduced.

In MySQL 5.7, the use of condition filtering estimates changes the query plan for Query 8, and the new query plan looks as follows:

As you see, part is now processed early. (We see that region is still processed first, but this is a small table with only 5 rows of which only one row matches its condition. Hence, it will not impact the fan-out of the join.) This new query plan takes 0.5 seconds to execute. In other words, execution time is reduced by 92% by changing the join order! The main saving is that, instead of going through all orders for customers in Europe, one will only look at orders of parts of a given type.

(The observant reader will have noticed another difference between the Visual EXPLAIN diagrams: The box around the tables is no longer present in the 5.7 diagram. If you look a Query 8, you notice that the many-table join is in a sub-query in the FROM clause; aka derived table. In MySQL 5.6 and earlier, the result of derived tables where materialized in a temporary table, before the main query was executed. The extra box in the 5.6 diagram, represents such a temporary table. In MySQL 5.7, derived tables will be merged into the outer query if possible, and the materialization of the derived table is avoided.)

To see the optimizer's estimates for condition filter effects, we can look at the filtered column of tabular EXPLAIN (some of the columns have been removed to save space):

id	select_type	table	type	key	rows	filtered	Extra
1	SIMPLE	region	ALL	NULL	5	20.00	Using where; Using temporary; Using filesort
1	SIMPLE	part	ALL	NULL	200000	10.00	Using where; Using join buffer (Block Nested Loop)
1	SIMPLE	lineitem	ref	i_l_partkey_suppkey	30	100.00	Using index condition
1	SIMPLE	supplier	eq_ref	PRIMARY	1	100.00	Using where
1	SIMPLE	n2	eq_ref	PRIMARY	1	100.00	NULL
1	SIMPLE	orders	eq_ref	PRIMARY	1	50.00	Using where
1	SIMPLE	customer	eq_ref	PRIMARY	1	100.00	Using where
1	SIMPLE	n1	eq_ref	PRIMARY	1	20.00	Using where

We can see that there are 4 tables for which the optimizer assumes some filtering; region, part, orders, and n1 (nation). The optimizer has three sources for its estimates:

1. Range estimates:

   For indexed columns, the range optimizer will ask the storage engine for an estimate as to how many rows are within a given range. This estimate will be pretty accurate. For our query, this is the case for the region and orders table. Europe is 1 out of 5 regions and the requested two year period contains 50% of the orders in the database.

2. Index statistics:

   For indexed columns, MySQL also keep statistics on the average number of rows per value (records_per_key). This can be used to estimate the filtering effect of conditions that refers to columns of preceeding tables in the join order. For our query this is the case for n1. The query has two conditions involving n1, but the condition on n_nationkey is used for the index look-up and will not contribute to extra filtering. On the other hand, the condition on n_regionkey will provide some extra filtering, and since records_per_key tells that there are 5 distinct values for this column, the estimated filtering effect will be 20%.

   The assumption is that values are evenly distributed. If the distribution is skewed, the filtering estimate will be less accurate. Another cause of estimation errors is that index statistics are based on sampling, so it may not precisely reflect the actual distribution of values.

3. Guesstimates:

   For non-indexed columns, MySQL does not have any statistics. The optimizer will then resort to heuristics. For equality conditions, the filtering effect is assumed to be 10%. The DBT-3 database does not have an index on part_type. Hence, the filtering effect for the part table will be set to 10%. This is actually a bit off since there are 150 different parts. However, in this case, just assuming that there is some filtering, made the optimizer choose a better plan.

Caveat

As shown in this blog post, taking condition filtering into account may give better query plans. However, as discussed, there is a risk that the filtering estimate are inaccurate, especially for conditions on non-indexed columns. Another cause of estimation errors is that it is assumed that columns are not correlated. Hence, when here are conditions on correlated columns, the optimizer will overestimate the filtering effect of those conditions.

We have got a few bug reports on performance regressions when upgrading from 5.6 to 5.7 that are caused by the optimizer overestimating the filtering effect. In most cases, this is because the query contains equality conditions on non-indexed columns with low cardinality, so the guesstimate of 10% is too optimistic. In my next blog post, I will discuss what can be done when this happens. We are also working on adding histograms to MySQL. Histograms will give a much better basis for estimating the filtering effects of conditions on non-indexed columns.