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A New MongoDB Sharding 
Architecture for Higher 
Availability and Better 
Resource Utilization 
Leif Walsh 
@leifwalsh
A Traditional 
MongoDB Cluster 
• 3 shards. 
• 3 replicas per shard.
A Traditional 
MongoDB Cluster 
• 3x write throughput. 
• 3x read throughput.
A Traditional 
MongoDB Cluster 
• 1 node can go down 
without losing availability.
A Traditional 
MongoDB Cluster 
• Data can survive 
destruction of 2 nodes.
General 
MongoDB Cluster 
• Sx write throughput. 
• Rx read throughput. 
• R/2 nodes can go down 
without losing availability. 
• Data can survive 
destruction of R-1 nodes. 
• S×R hardware & 
maintenance cost.
TokuMX: MongoDB with Fractal Trees 
• MongoDB fork. 
• Compression, performance, transactions. 
• Details about Fractal Trees after lunch.
TokuMX: MongoDB with Fractal Trees 
• Read-free Replication 
• Fast Updates 
• Optimized Sharding Migrations 
• Ark Consensus for Replication Failover 
• Partitioned Collections 
• Clustering Indexes & Primary Keys 
• tokutek.com/tokumx
Fractal Tree 
Performance Basics 
Writes are cheap: 
• O(1/B) I/Os per op. 
• ≈10k/s 
Reads are expensive: 
• Ω(1) I/O per op. 
• ≈100/s
Read-free Replication 
Updates are reads + writes. 
Secondaries can trust the primary, 
only do writes.
Read-free Replication 
Updates are reads + writes. 
Secondaries can trust the primary, 
only do writes. 
Looking at I/O utilization, 
secondaries are very cheap 
compared to primaries.
A Traditional 
TokuMX Cluster 
• 9 machines, only 3x 
throughput benefit. 
• Secondaries are 
under-utilized.
A TokuMX Cluster With 
Read-free Replication 
• 3x write throughput. 
• 3x read throughput. 
• (maybe separately)
A TokuMX Cluster With 
Read-free Replication 
• 1 node can go down 
without losing availability.
A TokuMX Cluster With 
Read-free Replication 
• Data can survive 
destruction of 2 nodes.
A TokuMX Cluster With 
Read-free Replication 
• Only 3x hardware cost, 
down from 9x.
Dynamo Architecture 
• Developed at Amazon. 
• Used by Cassandra, Riak, Voldemort. 
• Many components, I will focus on data 
partitioning. 
http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html
Dynamo Architecture 
• Servers are equal peers, not separate 
primaries and secondaries. 
• Store overlapping subsets of data 
(MongoDB shards store disjoint subsets). 
• Data partitioning determined by 
consistent hashing.
Dynamo Partitioning 
• N servers in a ring. 
• hash(K) is a location 
around the ring. 
• Store data for K on the 
next R servers on the 
ring.
Dynamo Partitioning 
• All nodes accept writes: 
~linear write scaling. 
• Data replicated R times: 
Rx read performance/ 
reliability.
Dynamo-style Sharding in TokuMX 
• Each node is primary for some 
chunks, secondary for others. 
• Nodes store overlapping 
subsets of the data set.
Dynamo-style Sharding in TokuMX 
• S primaries in the ring: 
Sx write throughput. 
• R copies of each chunk on 
separate machines: 
Rx read throughput, 
availability & recovery 
guarantees.
Dynamo-style Sharding in TokuMX 
• Adding a node: 
– Move one secondary from each 
of next 2 nodes to the new node. 
– Initialize a new replica set on the 
new node and next 2 nodes.
Future Work 
Chunk balancer is not 
sophisticated: 
• Adding/removing machines is 
rough, overloads the machine’s 
neighbors. 
• Can we use ideas from 
Cassandra & Riak to improve 
this? 
MongoDB architecture 
requires managing multiple 
processes on each machine. 
• We can do better with good 
tools. Talk to me if you want to 
write them.
Thanks! 
Come to my talk after lunch for details about 
Fractal Trees. 
leif@tokutek.com 
@leifwalsh 
tokutek.com/tokumx 
slidesha.re/13pxgH8

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Новая архитектура шардинга MongoDB, Leif Walsh (Tokutek)

  • 1. A New MongoDB Sharding Architecture for Higher Availability and Better Resource Utilization Leif Walsh @leifwalsh
  • 2. A Traditional MongoDB Cluster • 3 shards. • 3 replicas per shard.
  • 3. A Traditional MongoDB Cluster • 3x write throughput. • 3x read throughput.
  • 4. A Traditional MongoDB Cluster • 1 node can go down without losing availability.
  • 5. A Traditional MongoDB Cluster • Data can survive destruction of 2 nodes.
  • 6. General MongoDB Cluster • Sx write throughput. • Rx read throughput. • R/2 nodes can go down without losing availability. • Data can survive destruction of R-1 nodes. • S×R hardware & maintenance cost.
  • 7. TokuMX: MongoDB with Fractal Trees • MongoDB fork. • Compression, performance, transactions. • Details about Fractal Trees after lunch.
  • 8. TokuMX: MongoDB with Fractal Trees • Read-free Replication • Fast Updates • Optimized Sharding Migrations • Ark Consensus for Replication Failover • Partitioned Collections • Clustering Indexes & Primary Keys • tokutek.com/tokumx
  • 9. Fractal Tree Performance Basics Writes are cheap: • O(1/B) I/Os per op. • ≈10k/s Reads are expensive: • Ω(1) I/O per op. • ≈100/s
  • 10. Read-free Replication Updates are reads + writes. Secondaries can trust the primary, only do writes.
  • 11. Read-free Replication Updates are reads + writes. Secondaries can trust the primary, only do writes. Looking at I/O utilization, secondaries are very cheap compared to primaries.
  • 12. A Traditional TokuMX Cluster • 9 machines, only 3x throughput benefit. • Secondaries are under-utilized.
  • 13. A TokuMX Cluster With Read-free Replication • 3x write throughput. • 3x read throughput. • (maybe separately)
  • 14. A TokuMX Cluster With Read-free Replication • 1 node can go down without losing availability.
  • 15. A TokuMX Cluster With Read-free Replication • Data can survive destruction of 2 nodes.
  • 16. A TokuMX Cluster With Read-free Replication • Only 3x hardware cost, down from 9x.
  • 17. Dynamo Architecture • Developed at Amazon. • Used by Cassandra, Riak, Voldemort. • Many components, I will focus on data partitioning. http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html
  • 18. Dynamo Architecture • Servers are equal peers, not separate primaries and secondaries. • Store overlapping subsets of data (MongoDB shards store disjoint subsets). • Data partitioning determined by consistent hashing.
  • 19. Dynamo Partitioning • N servers in a ring. • hash(K) is a location around the ring. • Store data for K on the next R servers on the ring.
  • 20. Dynamo Partitioning • All nodes accept writes: ~linear write scaling. • Data replicated R times: Rx read performance/ reliability.
  • 21. Dynamo-style Sharding in TokuMX • Each node is primary for some chunks, secondary for others. • Nodes store overlapping subsets of the data set.
  • 22. Dynamo-style Sharding in TokuMX • S primaries in the ring: Sx write throughput. • R copies of each chunk on separate machines: Rx read throughput, availability & recovery guarantees.
  • 23. Dynamo-style Sharding in TokuMX • Adding a node: – Move one secondary from each of next 2 nodes to the new node. – Initialize a new replica set on the new node and next 2 nodes.
  • 24. Future Work Chunk balancer is not sophisticated: • Adding/removing machines is rough, overloads the machine’s neighbors. • Can we use ideas from Cassandra & Riak to improve this? MongoDB architecture requires managing multiple processes on each machine. • We can do better with good tools. Talk to me if you want to write them.
  • 25. Thanks! Come to my talk after lunch for details about Fractal Trees. leif@tokutek.com @leifwalsh tokutek.com/tokumx slidesha.re/13pxgH8