B is for Billion: Wordnik passes 9 billion record mark with MongoDB
1–10 of 15 posts
Re: B is for Billion: Wordnik passes 9 billion record mark with MongoDB
#2Very sparse on details. Are they partitioning the data? If so how? Is it MongoDB shards or at the application level or...? What's their average record size? Do they see a long-tail kind of access such that MongoDB really does keep the most commonly used records in memory all the time? How many machines are they serving from? What are their specs (particularly RAM)?
Re: B is for Billion: Wordnik passes 9 billion record mark with MongoDB
#3Very sparse on details. Are they partitioning the data? If so how? Is it MongoDB shards or at the application level or...? What's their average record size? Do they see a long-tail kind of access such that MongoDB really does keep the most commonly used records in memory all the time? How many machines are they serving from? What are their specs (particularly RAM)?
This is pretty well covered in this presentation: http://www.slideshare.net/fehguy/migrating-from-mysql-to-mon...
A few unanswered questions (sharding? Looks like none to me), but a good bit of numbers and specifics on their current setup.
Re: B is for Billion: Wordnik passes 9 billion record mark with MongoDB
#4Very sparse on details. Are they partitioning the data? If so how? Is it MongoDB shards or at the application level or...? What's their average record size? Do they see a long-tail kind of access such that MongoDB really does keep the most commonly used records in memory all the time? How many machines are they serving from? What are their specs (particularly RAM)?
[deleted]
Re: B is for Billion: Wordnik passes 9 billion record mark with MongoDB
#5Very sparse on details. Are they partitioning the data? If so how? Is it MongoDB shards or at the application level or...? What's their average record size? Do they see a long-tail kind of access such that MongoDB really does keep the most commonly used records in memory all the time? How many machines are they serving from? What are their specs (particularly RAM)?
Some of these questions are answered in a separate set of slides, which the author links to in the comments of the post. Hardware looks like a single server, 2x4 core CPUs, 32 GB of RAM, and a FC SAN.
Re: B is for Billion: Wordnik passes 9 billion record mark with MongoDB
#6Very sparse on details. Are they partitioning the data? If so how? Is it MongoDB shards or at the application level or...? What's their average record size? Do they see a long-tail kind of access such that MongoDB really does keep the most commonly used records in memory all the time? How many machines are they serving from? What are their specs (particularly RAM)?
This is pretty well covered in this presentation: http://www.slideshare.net/fehguy/migrating-from-mysql-to-mon... A few unanswered questions (sharding? Looks like none to me), but a good bit of numbers and specifics on their current setup.
The 47.7 queries per second figure in those slides surprised me a bit. I'd like to know more details there...is that for a single node, or the entire cluster?
Re: B is for Billion: Wordnik passes 9 billion record mark with MongoDB
#7Very sparse on details. Are they partitioning the data? If so how? Is it MongoDB shards or at the application level or...? What's their average record size? Do they see a long-tail kind of access such that MongoDB really does keep the most commonly used records in memory all the time? How many machines are they serving from? What are their specs (particularly RAM)?
Some of these questions are answered in a separate set of slides, which the author links to in the comments of the post. Hardware looks like a single server, 2x4 core CPUs, 32 GB of RAM, and a FC SAN.
Single node, eh? I thought MongoDB was not supposed to be run in that configuration.
Re: B is for Billion: Wordnik passes 9 billion record mark with MongoDB
#8Well I'd never heard of Wordnik but this is very cool and I'll be using it. This is like a respectable Dictionary.com+ Urban Dictionary. I don't see the category theory definition of a monad on it, though. Which is somewhat odd.
Re: B is for Billion: Wordnik passes 9 billion record mark with MongoDB
#9Very sparse on details. Are they partitioning the data? If so how? Is it MongoDB shards or at the application level or...? What's their average record size? Do they see a long-tail kind of access such that MongoDB really does keep the most commonly used records in memory all the time? How many machines are they serving from? What are their specs (particularly RAM)?
This is pretty well covered in this presentation: http://www.slideshare.net/fehguy/migrating-from-mysql-to-mon... A few unanswered questions (sharding? Looks like none to me), but a good bit of numbers and specifics on their current setup.
Video of this talk is available here: http://blip.tv/file/3621377
This article also has some details: http://www.royans.net/arch/mongodb-migration-from-mysql-at-w...
Re: B is for Billion: Wordnik passes 9 billion record mark with MongoDB
#10Earlier quoted context omitted.
Some of these questions are answered in a separate set of slides, which the author links to in the comments of the post. Hardware looks like a single server, 2x4 core CPUs, 32 GB of RAM, and a FC SAN.
Single node, eh? I thought MongoDB was not supposed to be run in that configuration.
They're probably running 1+ Mongod process/node per core - not exactly ideal though.