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Thousands of Indexes in the Cloud


Greplin searches:


- Greplin helps you search all your personal information, wherever it is. - As Michael Arrington of TechCrunch said, we’ve “attacked the other half of search.” - Greplin supports over a dozen services today, with more added constantly.

Requirements • Many inserts • Fewer searches • Low per-user cost 3

- We insert up to 5,000 documents/second - Average document size of 2KB-4KB - A fully loaded server is an Amazon c1.medium machine responsible for up to 80,000,000 3KB documents - Each machine has just 1.7GB of RAM! - Overall, we handle about 50M documents per GB of RAM with median search latencies around 200ms.

Memory • Per doc: 2 longs + 1 int +1 String (avg 5

letters) into the FieldCache, and average of 10 norm’d fields/doc

• 27 bytes/doc * 50M docs = 1.3GB


- Ranking requires pulling a few field values and norms into memory. - For 50M documents would require well over 1.3GB of memory. - Assuming an optimized index, searching the number of docs we have per machine with 1GB of RAM is impossible without swapping. - We benchmarked using a single-index + swapping: search times were multi-second.

“Virtual memory was meant to make it easier to program when data was larger than the physical memory, but people have still not caught on.” Poul-Henning Kamp,Varnish architect and coder. What’s Wrong With 1975 Programming


- Over the last decade, the trend has been to stop manually managing what goes on disk and what goes in RAM, instead trusting the operating system’s virtual memory and paging systems to swap data in/out appropriately. - For example, the caching HTTP proxy Varnish trusts the OS’s virtual memory, and is thus significantly simpler and faster than Squid, which tries to manage the what-belongs-inmemory vs what-belongs-on-disk itself. - This philosophy has been jokingly summarized as “You’re not smarter than Linus, so don’t try to be.”

We’re Smarter than Linus!*

* When we cheat 6

- Many signals (such as user logins) let us predict which users are likely to do searches better than the OS can. - By keeping each user’s data in a separate index, we save memory and improve performance. - We only keep open IndexSearchers for users who are likely to do searches.

Other Benefits • tar -cvzf user.tar.gz user && mv user.tar.gz • du -h • Smaller ‘corruption domain’


By keeping each user’s index separate, we can: - more easily move users between servers - figure out their space usage - ensure index corruption affects only one user

RAM Index • Deletion Filters • MultiSearcher • Flush planning


- Inspired by Zoie ( - All incoming documents are first added to a RAM Index. - A user search encompasses a ‘filtered’ view of the RAM Index, the currently flushing index, plus their disk index. - When the RAM index is ‘full’ we create a new RAM index. - We open IndexWriters for each user in turn and flush documents from RAM to disk. - Interesting cases including updates and deletions are handled with temporary filters on the disk index.

Amazon Cloud • • •

Script everything XFS+LVM expandability and snapshots are helpful Some pain is unavoidable EBS Performance 150000






Seq. Write

Seq. Read

Single EBS


Random Read

Instance Store

Random Write



More info at:

Other Cool Stuff •

‘kill -9’ any time with no data-loss via a Protocol Buffer Write Ahead Log

• • • •

Detect duplicate documents with Bloom Filter

Efficient result clustering and faceting

Dynamically sized SoftReference Cache Custom MergeScheduler Custom FieldCache for multi-valued or sparse fields


Some of this is open source:

Questions? Suggestions?

Robby Walker

Shaneal Manek @smanek 11

We’re hiring:

Greplin on Thousands of Indexes in the Cloud @ Lucene Revolution 2011  

Thousands of Indexes in the Cloud

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