1. The document discusses tips for using MongoDB, including avoiding regular expressions in queries, tokenizing field names to reduce index size, and using compound indexes instead of multiple single-field indexes. 2. It recommends tokenizing field names to reduce the size of indexes since MongoDB stores the full field name in indexes and regular expressions prevent indexes from being used in queries. 3. The document also suggests creating a single compound index on multiple fields instead of separate indexes on each field individually to allow a single index to be used across related queries.

1. #AYearWithMongoDB
The Tips
By: @rizkyabdilah
Email: rizky@abdi.la / rizky@mindtalk.com

2. What is MongoDB?
Document database
Schema-free
Memory mapped
Ok, the tips..

3. #1. Avoid RegExp
Example case, Mindtalk username:
Valid regex ([a-zA-Z0-9_.]+)

4. No!
db.user.ensureIndex({name: 1})
db.user.insert({name: “Danny”})
db.user.find({name: /dAnny/i})

5. Yes
db.user.ensureIndex({lower_name: 1})
db.user.insert({name: “Danny”,
lower_name: “danny”})
db.user.find({lower_name:
“dAnny”.toLowerCase()})

6. Comparisson
Index not used when query using RegExp

7. #2. Tokenize field name
Because of schema-free, every document
have a own schema (field-name or key in
json)

8. #2. Tokenize field name
Because of schema-free, every document
have a own schema (field-name or key in
json)
All of your key-name go into your
'valuable' RAM

9. #2. Tokenize field name
Because of schema-free, every document
have a own schema (field-name or key in
json)
All of your key-name go into your
'valuable' RAM
Imagine if you've very-very long key-
name, and it repeated in every
document. No need to explain

10. Example Case: Ndayak Stream

11. How we tackle this
Original Now:
Implementation:
Index size ~ 12GB
> 136 million doc
Space size ~ 15GB
Index size > 60GB
AvgObjSize 112
Space size > 24GB bytes
AvgObjSize 210

12. #3. Use single compound-key index
Instead of many single-key index

13. No
db.post.ensureIndex({user_id: 1, timepos:
-1})
db.post.ensureIndex({channel_id: 1,
timepos: -1})
Query find
db.post.find({user_id:
“uid”}).sort({timepos: -1})
db.post.find({channel_id:
“ch_id”}).sort({timepos: -1})

14. Yes
db.post.ensureIndex({user_id:1,
channel_id: 1, timepos: -1})
Query find, it will use the same index
db.post.find({user_id:
“uid”}).sort({timepos:-1})
db.post.find({channel_id:
“chid”}).sort({timepos:-1})

15. Later Discussions
Global write lock (introducing yield on
MongoDB 2.0)
Normalize 'own schema'
Sharding?

16. Thank You
Ps: don't forget to join channel
#EngineerBlog if you interested in advance :)