MongoDB performs best when documents are grouped together and data is written and indexed in a way that minimizes disk seeks. Specifically: 1) Group related documents together to reduce the number of random disk seeks needed to retrieve them. 2) Create indexes on fields that data is often inserted on to improve insertion performance and keep more data in memory. 3) Pre-allocate data on disk in the order it will be read to minimize disk seeks during queries. Reading sequentially stored data requires fewer seeks than randomly accessing documents.

1. [More]
MongoDB Performance
Tips
Colin Howe
@colinhowe
http://www.colinhowe.co.uk

2. MongoDB is a Sports Car

3. MongoDB is a Sports Car
Give it bad conditions and it crawls

4. 3 Tricks for
Minimising
Disk Access

5. Group Documents Together (1)
Stats system. Data = 3x RAM.
Single document per day
1.6s to read an entire year
Single document per month
0.3s to read an entire year

6. Group Documents Together (2)
Fewer random seeks
= Faster
Grouped documents
= Less overhead
= More in working set
See: http://bit.ly/foursquare-metrics-mongodb

7. Unusual Indices (1)
Index on metric type then date:
Inserts started at 10k/sec
Dropped to 2.5k/sec after 20m inserts
Index on date then metric type:
Inserts stayed at 10k/sec
No hit on query performance

8. Unusual Indices (2)
Only inserting to one side of index
Rebalancing hits less of the index
⇒ Less to flush to disk
⇒ More will be in memory

9. Pre-Allocate for Locality (1)
Pre-allocate data in read order
Data written in key then date order
6.6ms to query data for a year
Data written in date then key order
62ms to query data for a year

10. Pre-Allocate for Locality (2)
Data exists on disk in the order it is written
(ignoring resized documents)
Reading 12 random documents from disk
= 12 seeks
Reading 12 documents written at same time
= 1 seek + 11 sequential reads

11. MANDATORY
NOTICE:
Always benchmark
your use case

12. Questions?