The document discusses query planning in MongoDB. It covers the three stages of query planning: index selection, access plan generation, and analysis. An example .find() query is broken down to demonstrate how the _id index is selected and used to perform an index scan between bounds of 7 and infinity. The stages that queries can produce or consume data are also explained. Finally, plan ranking is briefly covered, where plans are run and the most efficient one is selected based on results produced per work unit executed.

1. Query Planning
Hari Khalsa
Query Therapist

2. What you’re in for…
1. Query system overview.
2. Walk through an example query.
3. Query Planning, revisited.
4. Plan Ranking

3. Querying is a two-step process.
(1) Planning
(2) Execution
What’s the best way to
execute this query?
Traverse the indexes,
get the results.

4. TAKE AN EXAMPLE
let’s look at the simplest case.

5. What’s in a .find()?
.find({_id: {$gt: 7}}, {_id: 1}).sort({_id: 1})

6. What’s in a .find()?
.find({_id: {$gt: 7}}, {_id: 1}).sort({_id: 1})
predicate
what to search for

7. What’s in a .find()?
.find({_id: {$gt: 7}}, {_id: 1}).sort({_id: 1})
projection
turns {_id: 3, x: 5} into {_id: 3}
predicate

8. What’s in a .find()?
.find({_id: {$gt: 7}}, {_id: 1}).sort({_id: 1})
projection
predicate
sort
by _id, ascending

9. { _id: 2, x: 3 }
{ _id: 3, x: 3 }
{ _id: 5, x: 5 }
{ _id: 8, x: 7 }
{ _id: 9, x: 4 }
What’s in a .find()?
.find({_id: {$gt: 7}}, {_id: 1}).sort({_id: 1})

10. { _id: 2, x: 3 }
{ _id: 3, x: 3 }
{ _id: 5, x: 5 }
{ _id: 8, x: 7 }
{ _id: 9, x: 4 }
What’s in a .find()?
.find({_id: {$gt: 7}}, {_id: 1}).sort({_id: 1})
{ _id: 8 }
{ _id: 9 }

11. QUERY PLANNING
selecting indexes, generating access plans, etc.

12. Query planning has three stages.
Index
Selection
Access
Plan Generation
Analysis
Sort, Project, etc.

13. Index Selection Access Analysis
There are three index “types”;
predicates are matched to indexes.
Geo
Text
B-Tree

14. .find({ _id: { $gt: 7 }}, { _id: 1 }).sort({ _id: 1 });
predicate
Which indexes
are useful?
Index Selection Access Analysis

15. .find({ _id: { $gt: 7 }}, { _id: 1 }).sort({ _id: 1 });
predicate
_id index is the
relevant index.
Index Selection Access Analysis

16. .find({ _id: { $gt: 7 }}, { _id: 1 }).sort({ _id: 1 });
Index Selection Access Analysis
predicate
Perform index scan on { _id: 1 } index
with bounds 7 < _id <= infinity.

17. .find({ _id: { $gt: 7 }}, { _id: 1 }).sort({ _id: 1 });
predicate
Perform index scan on { _id: 1 } index
with bounds 7 < _id <= infinity.
The predicate is fully covered by the index!
Index Selection Access Analysis

18. .find({ _id: { $gt: 7 }}, { _id: 1 }).sort({ _id: 1 });
predicate
What if multiple
indexes could work?
Index Selection Access Analysis

19. .find({ _id: { $gt: 7 }}, { _id: 1 }).sort({ _id: 1 });
Index Selection Access Analysis
projection sort
What other processing is needed?

20. .find({ _id: { $gt: 7 }}, { _id: 1 }).sort({ _id: 1 });
projection
What other processing is needed?
Index scan on {_id: 1} =>
Results are already sorted by {_id: 1}!
Index Selection Access Analysis

21. .find({ _id: { $gt: 7 }}, { _id: 1 }).sort({ _id: 1 });
What other processing is needed?
Index on {_id: 1} & only need _id field =>
No need to fetch documents from disk!
Index Selection Access Analysis

22. .find({ _id: { $gt: 7 }}, { _id: 1 }).sort({ _id: 1 });
The actual query solution tree:
RESULTS
Index Selection Access Analysis
IXSCAN
PROJ

23. ACCESS:
IT’S COMPLICATED
Index
Selection
Access
Plan Generation
Analysis
Sort, Project, etc.

24. Query stages can produce data,
or consume and produce data.

25. Predicate leaves are
data-providing stages.
IXSCAN
TEXT
GEONEAR
Predicate
Leaf

26. AND and OR stages
consume and produce data.
AND
PL
PL PL
OR

27. An “OR” query is indexed if
both of its children are indexed.
OR

28. OR
OR[x:1, z:1] with indices {x:1}, {z:1}

29. OR[x:1, z:1] with indices {x:1}, {z:1}
• OR (union)
• IXSCAN {x:1} from [1, 1]
• IXSCAN {z:1} from [1, 1]
OR

30. An “AND” query is indexed if at
least one of its children is indexed.
AND

31. AND
AND[x:1, y:1] with index {x:1}

32. AND
AND[x:1, y:1] with index {x:1}
• IXSCAN {x:1} from [1, 1]

33. AND
AND[x:1, y:1] with index {x:1}
• FETCH [ filter = {y:1} ]
• IXSCAN {x:1} from [1, 1]

34. What about
index intersection?
AND

35. RANKING PLANS
choosing the best plan for the query.

36. Run the plans, see which is best.

37. Run the plans, see which is best.
What do you mean, best?

38. Run the plans, see which is best.
What do you mean, best?
Time is not a reliable metric.

39. = look at one index key (IXSCAN)
= look at one more doc (COLLSCAN)
one “work” unit

40. Pick the plan with the highest
results produced
works executed

41. THAT’S THE PLAN.
Index
Selection
Access
Plan Generation
Analysis
Sort, Project, etc.

42. What does the future hold?
Stats! Better Index
Intersection

43. THANKS FOR
LISTENING!