Thursday, August 6, 2020

Over fetching in a DBMS

By over fetching I mean fetching irrelevant documents or fields while processing a query. By fetching I mean the data examined by the DBMS, not the result set returned to the user.  By irrelevant I mean documents and fields that won't change the query result if they don't exist. This also applies to a SQL DBMS after substituting column for field, row for document and collection for table. In the rest of this post I mostly use MongoDB names (documents, fields and collections). I might reuse some of the terminology from a post on predicates from Use The Index, Luke. That post is interesting.

I can refine the over fetching definition into four parts:

  • co-located
  • single-table
  • join
  • aggregation
  • other

Co-located over fetching occurs when irrelevant fields are read from a document or indexes. Columnar storage is a one way to avoid this for analytic workloads. Sometimes a covering index can reduce this for OLTP workloads.

For a collection C with fields a1, a2, ..., aN then db.C.find({}, {a1:1, a2:1, _id:0}) does co-located over fetching when a collection scan is used. An index on (a1, a2) avoids over fetching because the fields a3 ... aN are not in the index. The SQL version of the query is select a1, a2 from C.

Co-located over fetching also occurs when an index has fields that are needed by the query. For the query in the previous paragraph if there is an index on (a1, a2, a3, a4) and the index is used for the query then over fetching occurs because a3 and a4 are read by the DBMS but not needed.


Single-table over fetching occurs when irrelevant documents are read. A good index is one way to avoid this, especially for OLTP workloads. 

For this example the collection C has the documents:
  • { _id: 0, a1:7, a2:8, a3:9 }
  • { _id: 1, a1:7, a2:18, a3:19 }
  • { _id: 2, a1:27, a2:28, a3:29 }
The query db.C.find({a1: 7}) does single-table over fetching when there isn't an index on a1. In that case a collection scan is done that examines all docs but the doc with _id:2 is irrelevant. With an index on a1 then only the docs with _id:0 and _id:1 are examined and there is no over fetching. The SQL version of this query is select * from C where a1=7.

Single-table over fetching can occur with indexes. For the query db.C.find({ a1: 7, a2: 8}) with an index on a1 then docs with _id:0 and _id:1 are examined but _id:1 is irrelevant because the predicate on a2 excludes it. An index on (a1, a2) avoids the over fetching. The SQL version of this query is select * from C where a1=7 and a2=8.


Join over fetching occurs when join predicates filter documents. The docs that were filtered by the join predicate don't change the query result and figuring out how to avoid examining them prior to the join might improve performance.

With MongoDB $lookup is a left outer join and over fetching cannot occur for the input documents. But it can occur for docs in the from collection when there is no index, or no good index, on it. But explain doesn't show the access path for the from collection -- until SERVER-22622 is fixed.

Whether join over fetching occurs in SQL depends on the join (inner, left outer, right outer, etc). The example uses tables C1 and C2 and the query select * from C1, C2 where C1.x1 = C2.y1
  • C1 has the rows: (x1:1, x2:2), (x1:11, x2:12), (x1:21, x2:22)
  • C2 has the rows: (y1:1, y2:2), (y1:11, y2:12), (y1:31, y2:32)
Assume this is evaluated by scanning C1 and then probing C2 (nested loops join). If there is no index on C2.y1 then join over fetching occurs for C2 because (y1:31, y2:32) is examined by a table scan but filtered by the join predicate. Join over fetching occurs for C1 whether or not there is an index on C2.y1 because (x1:21, x2:22) is examined but filtered by the join predicate.


Aggregation over fetching occurs when docs are filtered by the aggregation operator semantics. The obvious ones are $max and $min in MongoDB (max and min in SQL).

For this example the collection C has the documents { _id: 0, a1:7, a2:8 }, { _id:1, a1:7, a2:9 } and the queries are:
  • MongoDB: db.C.aggregate([ { $group : { _id: "$a1", maxval : { $max : "$a2" } } } ])
  • SQL: select max(a2), a1 from C group by a1
Aggregation over fetching occurs for the doc with _id:0 because it doesn't have the max value of a2 for the group with a1:7. An index on (a1, a2) can avoid the aggregation over fetching but you should consult the DBMS documentation to understand whether that optimization has been implemented. MySQL has loose index scan for min/max and distinct, Postgres has recursive CTE (here and here) while MongoDB has DISTINCT_SCAN (for $first but not for $min or $max - see SERVER-40090).


Sometimes index-only queries aren't as index-only as you want them to be. Postgres relies on bits being set in the visibility map or it will fetch the base row from the heap. InnoDB relies on a different mechanism but there are cases where it too must fetch base row images from the PK (clustered) index for queries that appear to be index only.

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