Greplin searches: 2- 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 80,000,0003KB documents- Each machine has just 1.7GB of RAM!- Overall, we handle about 50M documents per GB of RAM with median search latenciesaround 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 4- 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 with1GB 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 http://www.varnish-cache.org/trac/wiki/ArchitectNotes 5- Over the last decade, the trend has been to stop manually managing what goes on disk andwhat goes in RAM, instead trusting the operating system’s virtual memory and pagingsystems to swap data in/out appropriately.- For example, the caching HTTP proxy Varnish trusts the OS’s virtual memory, and is thussigniﬁcantly simpler and faster than Squid, which tries to manage the what-belongs-in-memory vs what-belongs-on-disk itself.- This philosophy has been jokingly summarized as “You’re not smarter than Linus, so don’ttry 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 betterthan the OS can.- By keeping each user’s data in a separate index, we save memory and improveperformance.- 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’ 7By 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 8- Inspired by Zoie (http://sna-projects.com/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 thedisk index.
Amazon Cloud • Script everything • XFS+LVM expandability and snapshots are helpful • Some pain is unavoidable EBS Performance 150000 112500 KB/sec 75000 37500 0 Seq. Write Seq. Read Random Read Random Write Single EBS RAID10 EBS Instance Store RAID 0 EBS 9More info at: http://tech.blog.greplin.com/aws-best-practices-and-benchmarks
Other Cool Stuff • ‘kill -9’ any time with no data-loss via a Protocol Buffer Write Ahead Log • Detect duplicate documents with Bloom Filter • Dynamically sized SoftReference Cache • Custom MergeScheduler • Custom FieldCache for multi-valued or sparse ﬁelds • Efﬁcient result clustering and faceting 10Some of this is open source: https://github.com/Greplin