Challenges with MongoDB

Challenges with MongoDB

MongoDB Beijing 2012

Stone Gao

Monday, April 2, 2012

About Me

Tech Lead at Umeng.com


MongoDB is Awesome• Document-oriented storage

• Full Index Support

• Replication & High Availability

• Auto-Sharding

• Querying

• Fast In-Place Updates

• Map/Reduce

• GridFS


But...This talk is not Yet Another Talk about it’s Awesomeness

but

challenges with MongoDB


Outline

1. Global Write Lock Sucks

2. Auto-Sharding is not that Reliable

3. Schema-less is Over Rated

4. Community Contribution is Quite Low

5. Attitude Matters



http://www.clker.com/cliparts/3/3/5/D/X/b/locked-exclamation-mark-padlock-hi.png





mongod

db-ndoc2doc1

collection1

doc2doc1

collection2

db-1doc2doc1

collection1

doc2doc1

collection2

mysqld

db-ndoc2doc1

table1

doc2doc1

table2

db-1doc2doc1

table1

doc2doc1

table2

VS.

DB Process Lock VS. Row Lock

single global write lock for the entire server (process)


1. Global Write Lock SucksIntel SSD 320 RAID10 & mongostat

Nearly all data in RAM, lock ratio is pretty high and bunch of Queued Writes(qw)

39.5K Rread IOPS / 23K Write IOPS














Possible Solutions/Workarounds #1Wait for lock related issues on JIRA

•SERVER-1240 : Collection level lockinghttps://jira.mongodb.org/browse/SERVER-1240 Planning Bucket A Vote (154)

•SERVER-1241 : Intra collection locking (maybe extent)https://jira.mongodb.org/browse/SERVER-1241 Planning Bucket A Vote (25)

•SERVER-2563 : When hitting disk, yield lock - phase 1https://jira.mongodb.org/browse/SERVER-2563 Fixed in 1.9.1 Vote (25)

• any time we actually have to hit disk. so if a memory mapped page is not in ram, then we should yield

update by _id, remove, long cursor iteration

•SERVER-1169 : Record level lockinghttps://jira.mongodb.org/browse/SERVER-1169 Rejected Vote (1)

and more ...


https://jira.mongodb.org/browse/SERVER-863








Possible Solutions/Workarounds #2

One Collection per DB to Reduce Lock Ratio

But you can go no further

Use Auto-Sharding to the rescue ?


2. Auto-Sharding is not that Reliable

http://www.autoinsurancecompanies.com/wp-content/uploads/2011/11/reliable.jpg


http://images.sodahead.com/polls/001635729/1863780_overrated_answer_2_xlarge.jpeg


Auto-Sharding is not that Reliable


Problems with Auto-Sharding

• MongoDB can’t figure out how many docs in a collection after sharding

• Balancer dead lock [Balancer] skipping balancing round during ongoing split or move activity.)[Balancer] dist_lock lock failed because taken by....[Balancer] Assertion failure cm s/balance.cpp...

• Uneven shard load distribution

• ...

(Note: I did the experiment before 2.0. So some of the issues might be fixed or improved in new versions of MongoDB coz it’s evolving very fast)


0) Turn off the balancer (balancing won't understand your locations, but it shouldn't matter b/c you're using hashed shard keys)

1) Shard the empty collection over the shard key { location : 1, hash : 1 }

2) run db.runCommand({ split : "<coll>", middle : { "location":"DEN", "hash": "8000...0" }})

3) run db.runCommand({ split : "<coll>", middle : { "location":"SC", "hash": "0000...0" }})

4) move those empty chunks to whatever shards you want

- Greg Studer

Possible Solutions/Workarounds #1Manual Chunk Pre-Splitting

http://www.mongodb.org/display/DOCS/Splitting+Shard+Chunkshttps://groups.google.com/d/msg/mongodb-user/tYBFKSMM3cU/TiYtoOiNMgEJhttp://blog.zawodny.com/2011/03/06/mongodb-pre-splitting-for-faster-data-loading-and-importing/


http://www.mongodb.org/display/DOCS/Splitting+Shard+Chunks







https://github.com/twitter/gizzard/raw/master/doc/forwarding_table.png

SERVER-2001 : Option to hash shard key https://jira.mongodb.org/browse/SERVER-2001 Unresolved Fix Version/s: 2.1.1 Vote (27)

“The lack of hashing based read/write distribution amongst available shards is a huge issue for us now. We're actually considering implementing an app-side layer to do this but that obviously has a number of serious drawbacks.”- Remon van Vliet

“Seems like a good idea : we implemented hashed shard key on client-side : operation rate sky rocked ( x3 and less variability). Balancing is moreover quicker and done during our very heavy insertion process : perfect !”- Grégoire Seux







Plain-old Application Level Sharding

https://github.com/twitter/gizzard/raw/master/doc/forwarding_table.png




3. Schema-less is Over Rated





Schema-less is Over Rated

Schema-Free (schema-less) is not free. It means repeat the schema in every docs (records) !


Possible Solutions/Workarounds #1Use Short Key Names

ref : http://christophermaier.name/blog/2011/05/22/MongoDB-key-names

{"sequence":"AHAHSPGPGSAVKLPAPHSVGKSALR", "location":{ "chromosome":"19", "strand":"-", "begin":"51067007", "end":"51067085" }}

{"s":"AHAHSPGPGSAVKLPAPHSVGKSALR", "l":{ "c":"19", "s":"-", "b":"51067007", "e":"51067085" }}

1.6 billion documents

243 GB

183 GB

60 GB saved!


http://christophermaier.name/blog/2011/05/22/MongoDB-key-names

http://christophermaier.name/blog/2011/05/22/MongoDB-key-names


SERVER-863 : Tokenize the field names https://jira.mongodb.org/browse/SERVER-863 planned but not scheduled Vote (66)

“Most collections, even if they don’t contain the same structure , they contain similar. So it would make a lot of sense and save a lot of space to tokenize the field names.”

“The overall benefit as mentioned by other users is that you reduce the amount of storage/RAM taken up by redundant data in each document (so you can use less resources per request, hence gain more throughput and capacity), while importantly also freeing the developer from having to pick short and hard to read field names as a workaround for a technical limitation.”

- Andrew Armstrong





SERVER-164 : Option to store data compressed https://jira.mongodb.org/browse/SERVER-164 planned but not scheduled Vote (126)

“The way oracle handles this is transparent to the database server at the block engine level. They compress the blocks similar to how SAN store's handle it rather than at a record level. They use zlib type compression and the overhead is less than 5 percent. Due to the IO access reduction in both number of blocks touched, and amount of data transferred, the overall effect is a cumulative speed increase.

Should MongoDB do it this way? Maybe? But at the end of the day, the architecture must make Mongo more scalable, as well as increase the ability limit the storage footprint.”

- Michael D. Joy




4. Community Contribution is Quite Low

http://www.thompsoncrg.com/wp-content/themes/zoomtechnic/images/slide/img3.jpg




Community Contribution is Quite Low

https://github.com/mongodb/mongo/graphs/impacthttps://github.com/mongodb/mongo/contributors






5. Attitude Matters


5. Attitude Matters

MongoDB already has the sweetest API in the NoSQL world.

Wish more effort invested in fixing the Hard Problems : locking, sharding, storage engine...

http://www.mongodb.org/display/DOCS/SQL+to+Mongo+Mapping+Chart




We are hiring

• Backend Engineer (MongoDB, Hadoop, HBase, Storm, Scala, Java, Ruby, Clojure)

• Data Mining Engineer

• DevOps Engineer

• Front End Engineer

We are doing bigdata analytics

[email protected], April 2, 2012

mailto:[email protected]


Contact

• Email : [email protected] [email protected]

• Twitter: @stonegao




Thanks

Q & A


Technology

Challenges with MongoDB