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ID | IEP-24 | ||||||||||
Author | Vladimir Ozerov Ozerov | ||||||||||
Sponsor | Vladimir Ozerov Ozerov | ||||||||||
Created | 19 Jun 2018 | ||||||||||
Status |
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Table of Contents |
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The goal of this IEP is to avoid execution of SQL queries on partitions which do no contain relevant data.
SQL query may scan arbitrary set of cache values. In general case interested values may reside on every cluster node, so broadcast is needed. What is worse, when distributed joins are enabled, another broadcast for every broadcasted message may be needed. One of the most important optimization techniques Widely adopted optimization technique is so-called "partition pruning", allowing to calculate a set of partitions and nodes which will return empty result without actual query execution. Query parts are not executed on these nodes, increasing overall system throughput and providing nearly linear scaling.
Recent researches in distributed query processing assume that CPU is relatively cheap resource, while network IO and disk IO are equally costly operations. Partition pruning not only saves network requests, but also avoids unnecessary executionы of local query parts, preventing page cache trashing and additional disk reads (mostly random).
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When implemented it will provide the following benefits:
Partition pruning is already implemented in Apache Ignite in very simplified form [1]. Only WHERE condition with equality is considered and only for SQL queries without joins. We should expand it further.
[1] https://issues.apache.org/jira/browse/IGNITE-4509
This technique is used excessively by both distributed databases and classical RDBMS vendors (as a part of their partitioning/sharding solutions). First, query is analyzed and optionally re-written to allow for better partition pruning. Second, information of interesting partitions are extracted from query for every table (cache) and subquery (for complex multi-stage distributed queries).
WHERE conditions and JOIN-s are relatively simple targets for extract partition extraction. The main difficulty is subqueries, as they potentially introduce unlimited nesting what complicates analysis a lot. So the main goal of query rewrite is to minimize number of subqueries. Main techniques are JOIN conversion, correlation and materialization. Interestingly, all these techniques not only simplify partition extraction, but also allow optimizer to choose better execution plan for local execution, as JOINs are much easier to be analyzed and reordered than subqueries. As we shall see below, efforts are usually focused on non-correlated subqueries as they are the most costly for straightforward execution.
In the following sections we first explain how partitions could be extracted from SQL parts, and how certain query rewrite techniques could help us with it. Then we will describe how extracted partition info is assembled in a form of tree. Finally, we explain how partition info will be passed to thin clients, and how users will be able to control and monitor partition pruning.
Suppose that for every table we know it's affinity column. It is either PK or explicitly defined affinity column. Then we can analyze WHERE expressions related to the given tables to extract partition info. For JOINs we can compare affinity functions of two tables. If they are compatible, then we can "pass" partition information from one table to another.
Apache Ignite supports only hash-based sharding, so partition could be extracted only from equality conditions.
In further examples affinity function is denoted as '{...}'. Extracted partition is either concrete number, or query parameter index which will be converted to concrete number later. We will denote first type as "Pn" (e.g. P1, P2), and second as ":INDEX" (e.g. :1, :2). If partition cannot be extracted from condition, we will denote it as "ALL". Empty partition set is denoted as "EMPTY".
For equality we simply apply affinity function to the value.
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SELECT * FROM emp WHERE emp.id = 100
=> {id=100} => P1 |
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SELECT * FROM emp WHERE emp.id = :1
=> {id=:1} => :1 |
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SELECT * FROM emp WHERE emp.id != 100
=> {name!=100} => (ALL) |
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SELECT * FROM emp WHERE emp.name = :1
=> {name=:1} => (ALL) |
IN condition with list of values results in a merged list of affected partitions. IN condition with nested SELECT statement will not be supported for now.
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SELECT * FROM emp WHERE emp.id IN (100, :1)
=> ({name=100}, {name=:1}) => (P1, :1) |
Range conditions could be converted to IN statements if column is of integer type.
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SELECT * FROM emp WHERE emp.id BETWEEN 100 AND 102
=> {id BETWEEN 100 AND 102} => {100, 101, 102} => (P1, P2, P3) |
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SELECT * FROM emp WHERE emp.id > 100 AND emp.id <= 102
=> {id > 100 AND id <= 102} => {101, 102} => (P1, P2) |
Every WHERE expression can be represented as sequence conjunctive expressions separated by disjunctions. For two OR expressions we return disjunctive set. For AND expressions we return conjunctive set. Concrete partitions can be merged together. Partition placeholders can only be merged with ALL or EMPTY on the other side.
For AND condition there is a special rule. If two sides contain parameters, we cannot simplify them, because final result depend on resolved partitions. If left side contain parameters and right side contain only concrete values, then we can remove concrete values from the left side which are not present on the right side. And vice versa.
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(P1) AND (P2) => ()
(P1, P2) AND (P3, P4) => ()
(P1, P2) AND (P2, P3) => (P2)
(P1) AND (ALL) => (P1)
(P1, P2) AND (ALL) => (P1, P2)
(P1, P2) AND () => ()
(:1) AND (:2) => (:1) AND (:2)
(:1) AND (ALL) => (:1)
(:1) AND () => ()
(P1) AND (:2) => (P1) AND (:2)
(P1, :1) AND (P2) => (:1) AND (P2)
(P1, :1) AND (P2, :2) => (P1, :1) AND (P2, :2) |
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(P1) OR (P2) => (P1, P2)
(P1) OR (ALL) => (ALL)
(P1) OR () => (P1)
(P1, P2) OR (P2, P3) => (P1, P2, P3)
(:1) OR (:2) => (:1, :2)
(P1, :1) OR (P2, :2) => (P1, P2, :1, :2) |
Joins are very common, so it is crucial to support partition extraction for them as well. General solution might be extremely complex, so we need to define reasonable bounds where optimization is applicable, and improve them iteratively in future. We start with query AST obtained from parser. Proposed flow to extract partitions is explained below. Some of these steps could be merged to improve performance.
REPLICATED
and do not have node filter, then mark it as "ANY" and remove from the tree, as it doesn't affect JOIN outcome. Otherwise - exit, no need to bother with custom filters.RendezvousAffintiyFunction
is deterministic, while FairAffinityFunction
is not)PARTITIONED
caches, only equi-joins, where every table is assigned to a single co-location group.REPLICATED
caches with "ANY" policy should be eliminated as follows:
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(P1, :2) AND (ANY) => (P1, :2)
(P1, :2) OR (ANY) => (P1, :2) |
If partition tree contain rules from different co-location groups, then exit.
It is not easy to extract partitions from subqueries. But we can rewrite certain subqueries to joins with a technique called "join conversion". Important The goal is to convert a subquery to join if possible. Important prerequisite is that number of resulting rows is not changed. Outer(FK) → inner(PK) joins are possible. E.g. subqueries in SELECT (aka "derived table") and FROM clauses.
Example 1: JOIN conversion for derived table
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SELECT emp.name FROM emp, dept WHERE emp.salary>1000 AND emp.dept_id=dept.id AND dept.state='CA' |
Another example where inner subquery return at most one row is outer(PK) → inner(FK GROUP BY PK) case. Subquery is not eliminated in this case, but IN expression is replaced with more comfortable JOIN.
[1] https://mariadb.com/kb/en/library/table-pullout-optimization/
[2] https://dev.mysql.com/doc/refman/8.0/en/subquery-optimization-with-exists.html
Partitions could be calculated not only on the server side, but on clients as well. This way thin clients could be able to send requests directly to affected nodes, reducing latency. To achieve this we first define serializable partition tree model:Example 4: Replacing IN with JOIN
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SELECT dept.name
FROM dept
WHERE dept.id IN (SELECT emp.dept_id FROM emp GROUP BY emp.dept_id HAVING COUNT(*)>10) |
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SELECT dept.name
FROM dept JOIN (SELECT emp.dept_id FROM emp GROUP BY emp.dept_id HAVING COUNT(*)>10) emp_subquery ON dept.id=emp_subquery.dept_id |
Uncorrelated subquery may need to scan huge result set. If underlying cache is PARTITIONED
, then multiple nodes need to be scanned. If underlying cache is REPLICATED
, then scan is local. If previous optimizations are not applicable, we can try to push-down condition from outer table to subquery. If condition is equality and both outer and inner columns are co-located, then potentially distributed query is reduced to co-located query. The downside is that subquery must be evaluated multiple times, once for every tuple from outer relation. This might be less than optimal comparing to subquery materialization explained below. MySQL refer to this as IN-to-EXISTS optimization [2].
Example 5: Converting non-correlated subquery to correlated via condition push-down
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SELECT emp.name
FROM emp
WHERE emp.dept_id IN (SELECT id FROM dept WHERE state='CA') |
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SELECT emp.name
FROM emp
WHERE EXISTS (SELECT id FROM dept WHERE dept.id=emp.dept_id AND state='CA') |
If subquery is not correlated, then it can be executed only once. Result is cached in temporary table and then re-used. If expected number of returned records is small, then it makes sense to execute the request before other query parts, and send obtained result to other map nodes. If result is scalar, then entire query is replaced with a single result.
This way some queries which were too complex to extract target partitions from may be reduced to simpler form, where extraction is possible.
[1] https://mariadb.com/kb/en/library/table-pullout-optimization/
[2] https://dev.mysql.com/doc/refman/8.0/en/subquery-optimization-with-exists.html
TODO
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interface PartitionNode {
Collection<Integer> apply(Object[] args);
}
class PartitionGroup implements PartitionNode {
Collection<Object> parts; // Concrete partitions, arguments or both.
}
class PartitionExpression implements PartitionNode {
PartitionNode left;
PartitionNode right;
} |
Partition tree is enriched with AffinityTopologyVersion
it was built on, and affinity function descriptor. Descriptor can only be defined for well-known affinity functions, such as RendezvousAffinityFunction
.
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class PartitionInfo {
PartitionNode tree;
AffintiyTopologyVersion affTopVer;
AffinityFunctionDecriptor affFunc;
} |
When client executes request for the first time, the following sequence of actions happen:
Client saves partition tree and re-use it for further requests as follows:
If partition tree is extracted form the query successfully, then two types of optimizations are possible:
It is very important to let user know if partition pruning is applicable to query for performance tuning. For every cached two-step query we may expose the following information:
Also we need to let user disable this optimization. Otherwise a bug in implementation may lead to incorrect results with no workarounds. System on configuration property could be used for that.TODO
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