1. Alex Bevilacqua
The Sights (and Smells) of a Bad Query
@alexbevi

2. INTRODUCTION
{ context: “speaker”, smell: “pleasant” }

3. Alex Bevilacqua
Technical Services Engineer, MongoDB

4. IT’S ME … MARIO!
ALEX!
• Application Developer
• Development Lead
• System Architect
• Technical Services
Engineer
• Author
• MongoDB Fanboy

5. Sights?

6. What we’ll be looking at
• Generic looks at some queries
• Introduction to some tooling
• High level discussion
• Not focusing on “solutions”

7. Smells?

8. What stinks?
https://en.wikipedia.org/wiki/Code_smell
A code smell is any characteristic in the source code of
a program that possibly indicates a deeper problem.
Code smells are usually not bugs; they are not technically incorrect and
do not prevent the program from functioning.
Instead, they indicate weaknesses in design that may slow down
development or increase the risk of bugs or failures in the future.

9. Scenario
• Atlas M10 Cluster
• 2-3M documents
• Generated and imported
using a template
(mgeneratejs)
• Ran some adhoc queries

10. SIMPLE QUERY
{ context: “find”, smell: “good?” }

11. Our Dataset

12. What’re we looking for?
db.users.find({ age: 38 })

13. Let’s find some data!
• Found 53,516 Results
• Took a few seconds longer
than we’d like ☹

14. Who’s seen
this?

15. ALERTS =

16. Let’s see that again … as an Explain Plan
• Query time higher than we
would expect
• COLLSCAN
• Large number of documents
scanned (entire collection!)

17. COLLSCAN =

18. Adding an Index
db.users.createIndex({ age: 1 })

19. Explain that again please
• Performance greatly improved
• IXSCAN
• Fewer documents scanned

20. QUERYING
MULTIPLE FIELDS
AND SORTING
{ context: { $in: [“find”, “sort”] }, smell: “good?” }

21. What’re we looking for?
db.users.find({ age: { $gt: 25 }, “address.state”: “UT” }).sort({ name: 1 })

22. Let’s find some data – in order this time
• Looking for everyone older than
25 from Utah, sorted by name
• IXSCAN …. But slow
• Far more keys and documents
examined than returned (we’ll
come back to this one)
• In memory sort!

23. SORT
=

24. We know we
need an
index!

25. Let’s try that again …

26. What’s the explain telling us?
• IXSCAN using the
expected index
• Faster execution
• Same number of
documents scanned
and returned …
• In memory sort???

27. (E)quality – (S)ort – (R)ange
db.users.find({ age: { $gt: 25 }, ‘address.state’: ‘UT’ }).sort({ name: 1 })
Rang
e
Equality Sort

28. Non-ESR
Compound =
Indexes

29. METRICS AND
LOGS AND
CHARTS …
OH MY!
{ context: “analysis”, smell: “good?” }

30. What can the Atlas Metrics tell us?
• Sort operation that
couldn’t use an index
• Ratio of documents
scanned to the number of
documents returned
• Increased average
read/write time per
operation

31. Sharp rise
in one or =
more metrics

32. Remember
this?

33. Check the Performance Advisor

34. Get your logs

35. Plotting queries by execution time
... COMMAND [conn416] command data.users command: aggregate {
aggregate: "users", pipeline: [ { $match: {} }, { $skip: 0 }, { $group: { _id:
null, n: { $sum: 1 } } } ], ... planSummary: COLLSCAN keysExamined:0
docsExamined:2585100 cursorExhausted:1 numYields:20196
nreturned:1 reslen:243 ... 1892ms
mlogfilter *.log --markers none | mplotqueries --logscale

36. Queries slower
than slowms =

37. Plotting queries by docsExamined/n
… COMMAND [conn1136] command data.users … { find: "users",
filter: { age: { $gt: 25.0 }, address.state: "UT" }, sort: { name: 1.0 }, …
planSummary: IXSCAN { age: 1 } cursorid:73909299301
keysExamined:2154391 docsExamined:2154391 hasSortStage:1
numYields:17029 nreturned:101 … 94212ms
mlogfilter *.log --markers none | mplotqueries --type docsExamined/n

38. Large
docsExamined/n =

39. What can stink?
• Alerts (from Atlas, Ops Manager, Cloud Manager)
• COLLSCAN (in logs or explain plans)
• SORT (in explain plans or in logs as hasSortStage)
• Non-ESR Compound Indexes
• Sharp rise in one or more metrics (in Atlas, Ops Manager or
Cloud Manager)
• Queries slower than slowms
• Large docsExamined/n ratio

40. Links
Notes/links from this talk available at:
http://bit.ly/ab-mdbw19
alexbevi
alexbevi
@alexbevi

42. THANK YOU FOR ATTENDING!
QUESTIONS?