3 ms·

Technically the different copies of the buckets aren't "identical" since they may get populated in unserialized ways (they may store data in different orders) b

Technically the different copies of the buckets aren't "identical" since they may get populated in unserialized ways (they may store data in different orders) but yes they all store the same set of blocks. Note that I just described the replicated data storage in the blog post. Approximately 24 hours later these volumes get packaged up and erasure coded into a much more efficient storage representation, so the initial replicated writes are just there for high durability and efficient writes while the buckets are mutable. I'm not sure I understand exactly what your question is but I think it's asking why we use 1GB buckets allocated all over the place rather than just picking a set of disks and filling them up RAID-style? This is a pretty important point: If we picked X nodes and stored all the same data on them then if we lost one of those nodes we'd only have X-1 other nodes to re-replicate from. This sets up a hotspot in the system and limits how quickly we can re-replicate. This can be a pretty big deal if we lose a machine storing 1PB of data - we need to amortize that reconstruction cost over a large number of NICs and disk spindles (this is covered in more detail in the blog post). The other advantage of having "small" buckets is that they're a manageable unit for combining into larger volumes when erasure coding, or for tracking partial repair operations, or for garbage collection and compaction when data is deleted. Seems like it might be worth going into more detail on erasure coding in a future blog post.

jhartmann 10y ago
My main questions was why always store the same blocks together, and you definitely answered that. Interesting approach. It definitely achieves your goal of spreading out the load. I think this can be achieved without making sure you store the blocks always together though, and it might help move towards a less coordinated system. Here is how I solved the problem: You can arrange each instance (you call it cell in your post) so it has different pools of storage. Maybe something that might make sense is each machine at the top of each rack is one pool, all the next machines down is another pool, and so on. All replication is always done across a pool. All the machines in a pool are stored to in a round robin fashion at the client. The end user can choose how many pools they want to replicate over. Abstract each of the machines as a storage unit. Each storage unit actually contains a series of 'slabs' which are largish files (32 gb in size generally), that grow in some large increment and close out when they hit a high watermark. Each write to a machine goes to a different writable slab in a round robin fashion. Each server knows the path and the contents of each slab, and on disk failure all the individual pieces are replicated to another set of open slabs just like the blocks came in fresh (with an exclusion of the slabs that already have a copy). Since all requests are balanced across the pools, and each client has a different round robin list it manages locally the individual pieces are evenly distributed over the other machines in the other pools. I tried really hard though to optimize for a zero master situation, so my clients handle contacting all the different machines and doing caching and retrying. So it sounds like I had different goals than you guys did, so I'm not surprised your have different trade offs.
- james_cowling 10y ago
  Yep looks like we may have had slightly different goals here. Another one of our design requirements was that we wanted to be able to move data around within the system (repair operations, rebalancing, encoding, compaction etc) without dealing with the metadata overhead of addressing hundreds of billions of blocks. Statically mapping a block to a 1GB bucket allows us to manage placement decisions on the granularity of buckets rather than orders of magnitude more individual files/blocks.
  - jorangreef 10y ago
    Thanks for sharing a great post. Do you think a 512MB bucket might be more optimal compared to a 1GB bucket? 2x less data required to be read when restoring an erasure coded bucket, and 2x quicker restore times, for only a 2x increase in bucket metadata costs? Erasure coded bucket slices (around 85MB per slice assuming 6/9 Reed Solomon) should be just right to amortize disk seeks.
    - james_cowling 10y ago
      Thanks! It's always a bit of a tradeoff. A 512MB bucket size would double the size of the Replication Table and we want make sure we keep the entire working set in the buffer cache. 1GB also has the advantage of filling up slower, which gives us more time to close full buckets and create new ones as needed. That said, 1GB is a bit of an arbitrary number which just happens to work pretty well. 512MB might work just fine. The exact number isn't as significant since typically we lose a whole disk at a time and thus needs to recover a certain number of TB regardless of the bucket size.
      - jorangreef 10y ago
        Thanks, that makes sense!

jhartmann 10y ago

My main questions was why always store the same blocks together, and you definitely answered that. Interesting approach. It definitely achieves your goal of spreading out the load. I think this can be achieved without making sure you store the blocks always together though, and it might help move towards a less coordinated system. Here is how I solved the problem: You can arrange each instance (you call it cell in your post) so it has different pools of storage. Maybe something that might make sense is each machine at the top of each rack is one pool, all the next machines down is another pool, and so on. All replication is always done across a pool. All the machines in a pool are stored to in a round robin fashion at the client. The end user can choose how many pools they want to replicate over. Abstract each of the machines as a storage unit. Each storage unit actually contains a series of 'slabs' which are largish files (32 gb in size generally), that grow in some large increment and close out when they hit a high watermark. Each write to a machine goes to a different writable slab in a round robin fashion. Each server knows the path and the contents of each slab, and on disk failure all the individual pieces are replicated to another set of open slabs just like the blocks came in fresh (with an exclusion of the slabs that already have a copy). Since all requests are balanced across the pools, and each client has a different round robin list it manages locally the individual pieces are evenly distributed over the other machines in the other pools. I tried really hard though to optimize for a zero master situation, so my clients handle contacting all the different machines and doing caching and retrying. So it sounds like I had different goals than you guys did, so I'm not surprised your have different trade offs.

james_cowling 10y ago
Yep looks like we may have had slightly different goals here. Another one of our design requirements was that we wanted to be able to move data around within the system (repair operations, rebalancing, encoding, compaction etc) without dealing with the metadata overhead of addressing hundreds of billions of blocks. Statically mapping a block to a 1GB bucket allows us to manage placement decisions on the granularity of buckets rather than orders of magnitude more individual files/blocks.
- jorangreef 10y ago
  Thanks for sharing a great post. Do you think a 512MB bucket might be more optimal compared to a 1GB bucket? 2x less data required to be read when restoring an erasure coded bucket, and 2x quicker restore times, for only a 2x increase in bucket metadata costs? Erasure coded bucket slices (around 85MB per slice assuming 6/9 Reed Solomon) should be just right to amortize disk seeks.
  - james_cowling 10y ago
    Thanks! It's always a bit of a tradeoff. A 512MB bucket size would double the size of the Replication Table and we want make sure we keep the entire working set in the buffer cache. 1GB also has the advantage of filling up slower, which gives us more time to close full buckets and create new ones as needed. That said, 1GB is a bit of an arbitrary number which just happens to work pretty well. 512MB might work just fine. The exact number isn't as significant since typically we lose a whole disk at a time and thus needs to recover a certain number of TB regardless of the bucket size.
    - jorangreef 10y ago
      Thanks, that makes sense!