Impacts of Sharding, Partitioning, Encoding, and Sorting on Distributed Query Performance
1 like724 views
InfluxDB IOx Tech Talks This talk presents a design of a distributed database system that splits data to gain query performance. The talk will define four main properties of data splitting: sharding, partitioning, sorting, and encoding; and then delve into examples to show their impacts on query performance.
Ad
More Related Content
What's hot (20)
Similar to Impacts of Sharding, Partitioning, Encoding, and Sorting on Distributed Query Performance (20)
Ad
More from InfluxData (20)
![Ward Bowman [PTC] | ThingWorx Long-Term Data Storage with InfluxDB | InfluxDa...](https://cdn.slidesharecdn.com/ss_thumbnails/influxdays-221027185325-5d2f430b-thumbnail.jpg?width=560&fit=bounds)
![Scott Anderson [InfluxData] | New & Upcoming Flux Features | InfluxDays 2022](https://cdn.slidesharecdn.com/ss_thumbnails/influxdays2022-fluxupdates-scott-221021210238-9d323cba-thumbnail.jpg?width=560&fit=bounds)
![Steinkamp, Clifford [InfluxData] | Closing Thoughts | InfluxDays 2022](https://cdn.slidesharecdn.com/ss_thumbnails/influxdays2022closingthoughtsday2-221020220104-abde55ea-thumbnail.jpg?width=560&fit=bounds)
![Steinkamp, Clifford [InfluxData] | Welcome to InfluxDays 2022 - Day 2 | Influ...](https://cdn.slidesharecdn.com/ss_thumbnails/influxdays2022welcometoday2-221020215815-c8463942-thumbnail.jpg?width=560&fit=bounds)
![Steinkamp, Clifford [InfluxData] | Closing Thoughts Day 1 | InfluxDays 2022](https://cdn.slidesharecdn.com/ss_thumbnails/influxdays2022closingthoughtsday1-221020215301-f8040e1f-thumbnail.jpg?width=560&fit=bounds)
![Paul Dix [InfluxData] The Journey of InfluxDB | InfluxDays 2022](https://cdn.slidesharecdn.com/ss_thumbnails/2022-11-02influxdays-journeyofinfluxdb-221020214252-ff7c76c5-thumbnail.jpg?width=560&fit=bounds)
Ad
Recently uploaded (20)
Impacts of Sharding, Partitioning, Encoding, and Sorting on Distributed Query Performance
- 1. Impacts of Sharding, Partitioning, Encoding, and Sorting on Distributed Query Performance Nga Tran Staff Engineer, InfluxData July 14, 2021
- 2. ● InfluxData - Staff Engineer ● Tableau/Salesforce (2 years) ○ Sr. Manager of Automatic Statistics ● Vertica RDBMS (over a decade) ○ Engineer of Query Optimizer ○ Director of Engineering (R&D) ● ELCA (4 years)
- 3. Outline ● Non-distributed vs Distributed Databases ● Splitting Data to Gain Query Performance ○ Sharding, Partitioning, Encoding, and Sorting ● Impacts of different data setups on Query Performance
- 4. Distributed Database Non-Distributed DB: 1-node cluster ● 1 machine ● Data is loaded & then queried on that node Distributed DB: Cluster of many nodes ● Several machines shares the work ● Data is horizontally split between nodes ● Data is queried from all nodes Node Non-Distributed DB Node 1 Node 2 Node n N nodes, each plays the same role and talks to each other Distributed DB Row 1 Row 2 …….. Row a Row a+1 Row a+2 ……….. Row b Row x+1 Row x+2 ……….. Row n
- 5. Distributed Database Non-Distributed DB: 1-node cluster ● 1 machine ● Data is loaded & then queried on that node Distributed DB: Cluster of many nodes ● Several machines shares the work ● Data is horizontally split between nodes ● Data is queried from all nodes → How to split data to gain query performance? Node Non-Distributed DB Node 1 Node 2 Node n N nodes, each plays the same role and talks to each other Distributed DB Row 1 Row 2 …….. Row a Row a+1 Row a+2 ……….. Row b Row x+1 Row x+2 ……….. Row n
- 6. Splitting Data to Gain Query Performance ● Sharding ○ Horizontally split a table into N non-overlapping shards ■ → each node will (equally) share 1/n of the workload: ● Load 1/n data to each node ● Query: join & group-by on each node share 1/n workload ● Partitioning ○ Each shard is further split into smaller partitions for better data filtering, deleting, fanning out, local parallelism ● Encoding ○ Each column is encoded (sorted & compressed) to further help on join, filtering, group-by, order-by
- 7. Splitting Data to Gain Query Performance ● Sharding ○ Horizontally split a table into N non-overlapping shards ■ → each node will (equally) share 1/n of the workload: ● Load 1/n data to each node ● Query: join & group-by on each node share 1/n workload ● Partitioning ○ Each shard is further split into smaller partitions for better data filtering, deleting, fanning out, local parallelism ● Encoding ○ Each column is encoded (sorted & compressed) to further help on join, filtering, group-by, order-by → Let us dig into examples
- 8. Line_Item o_okey o_date o_pri 1 2021.05.01 2 2 2021.05.01 1 3 2021.05.02 1 4 2021.05.02 3 5 2021.05.02 1 Examples: Two tables Order & Line_Item Order l_okey l_name l_price l_shipdate 1 desk 100 2021.05.07 1 chair 50 2021.05.03 1 monitor 130 2021.05.03 1 mouse 10 2021.05.07 2 pot 20 2021.05.01 2 pan 25 2021.05.04 3 shirt 30 2021.05.10 4 bike 120 2021.05.04 4 helmet 30 2021.05.10 5 kayak 200 2021.05.05 5 lifevest 20 2021.05.02
- 9. Sharded : Order: (o_okey % 2) & Line_Item: (l_okey % 2) o_okey o_date o_pri 1 2021.05.01 2 3 2021.05.01 1 5 2021.05.02 1 l_okey l_name l_price l_shipdate 1 desk 100 2021.05.07 1 chair 50 2021.05.03 1 monitor 130 2021.05.03 1 mouse 10 2021.05.07 3 shirt 30 2021.05.2 5 kayak 200 2021.05.07 5 lifevest 20 2021.05.02 o_okey o_date o_pri 2 2021.05.01 1 4 2021.05.02 3 l_okey l_name l_price l_shipdate 2 pot 20 2021.05.01 2 pan 25 2021.05.04 4 bike 120 2021.05.04 4 helmet 30 2021.05.10 Examples: 2-node cluster Node 1 Node 2 Order Line_Item Line_Item Order
- 10. Sharded : Order: (o_okey % 2) & Line_Item: (l_okey % 2) Partitioned : Order: (o_date) & Line_Item: (l_shipdate) o_okey o_date o_pri 1 2021.05.01 2 3 2021.05.01 1 5 2021.05.02 1 l_okey l_name l_price l_shipdate 3 shirt 30 2021.05.2 5 lifevest 20 2021.05.02 1 chair 50 2021.05.03 1 monitor 130 2021.05.03 1 desk 100 2021.05.07 1 mouse 10 2021.05.07 5 kayak 200 2021.05.07 o_okey o_date o_pri 2 2021.05.01 1 4 2021.05.02 3 l_okey l_name l_price l_shipdate 2 pot 20 2021.05.01 2 pan 25 2021.05.04 4 bike 120 2021.05.04 4 helmet 30 2021.05.10 Examples: 2-node cluster Node 1 Node 2 Order Line_Item Line_Item Order
- 11. Sharded : Order: (o_okey % 2) & Line_Item: (l_okey % 2) Partitioned : Order: (o_date) & Line_Item: (l_shipdate) Encoded & Sorted : Order: (o_okey) & Line_Item: RLE(l_okey) o_okey o_date o_pri 1 2021.05.01 2 3 2021.05.01 1 5 2021.05.02 1 l_okey l_name l_price l_shipdate (3,1) shirt 30 2021.05.2 (5,1) lifevest 20 2021.05.02 (1, 2) chair 50 2021.05.03 monitor 130 2021.05.03 (1,2) desk 100 2021.05.07 mouse 10 2021.05.07 (5,1) kayak 200 2021.05.07 o_okey o_date o_pri 2 2021.05.01 1 4 2021.05.02 3 l_okey l_name l_price l_shipdate (2,1) pot 20 2021.05.01 (2,1) pan 25 2021.05.04 (4,1) bike 120 2021.05.04 (4,1) helmet 30 2021.05.10 Examples: 2-node cluster Node 1 Node 2 Order Line_Item Line_Item Order
- 12. Impacts of the setups on query performance
- 13. Examples: Query SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_okey, o_date,o_pri ORDER BY revenue desc, o_date;
- 14. Examples: Query - Do the shards help? SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_okey, o_date,o_pri ORDER BY revenue desc, o_date;
- 15. Sharded : Order: (o_okey % 2) & Line_Item: (l_okey % 2) o_okey o_date o_pri 1 2021.05.01 2 3 2021.05.01 1 5 2021.05.02 1 l_okey l_name l_price l_shipdate 1 desk 100 2021.05.07 1 chair 50 2021.05.03 1 monitor 130 2021.05.03 1 mouse 10 2021.05.07 3 shirt 30 2021.05.2 5 kayak 200 2021.05.07 5 lifevest 20 2021.05.02 o_okey o_date o_pri 2 2021.05.01 1 4 2021.05.02 3 l_okey l_name l_price l_shipdate 2 pot 20 2021.05.01 2 pan 25 2021.05.04 4 bike 120 2021.05.04 4 helmet 30 2021.05.10 Back to Shard setup Node 1 Node 2 Order Line_Item Line_Item Order
- 16. Examples: Query - Do the shards help? SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_okey, o_date, o_pri ORDER BY revenue desc, o_date; YES ● Join: l_okey = o_key ○ → all odd keys in node 1 and even keys in node 2 ○ → Node 1 and node 2 join data on their local node. No need to shuffle data between nodes before joining. ● Group By: l_okey, o_date, o_pri ○ → Similarly, same group-by keys are in the same nodes. Each node can aggregate data without the need to reshuffle data
- 17. Examples: Query - Do the shards help? SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_key, o_date, o_pri ORDER BY revenue desc, o_date; What if Order not sharded on o_okey & Line_item not sharded on l_okey?
- 18. Examples: Query - Do the shards help? SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_key, o_date, o_pri ORDER BY revenue desc, o_date; What if Order not sharded on o_okey & Line_item not sharded on l_okey? ● Join: l_okey = o_key ○ → Need to reshuffle data so same join keys land on the same nodes before joining. Many ways: ■ Reshard on the fly both Order on o_okey and Line_Item on l_okey ■ Broadcast small table (o_okey) to other nodes ● Group By: l_okey, o_date, o_pri ○ → If after the join the data is shared on l_okey, nothing is needed. Otherwise, either: ■ Reshard data on l_okey to 2 nodes ■ Send everything to one node to do the final group-by
- 19. Examples: Query - Do the shards help? SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_key, o_date, o_pri ORDER BY revenue desc, o_date; What if Order not sharded on o_okey & Line_item not sharded on l_okey? ● → Not sharded on join keys will lead to extra on-the-fly reshard or broadcast cost ● → Not already (re-)sharded on group-by keys before the group-by operator will lead to either ○ Reshard or ○ The final node has to do all the group-by work
- 20. Examples: Query - Do the partitions help? SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_okey, o_date,o_pri ORDER BY revenue desc, o_date;
- 21. Sharded : Order: (o_okey % 2) & Line_Item: (l_okey % 2) Partitioned : Order: (o_date) & Line_Item: (l_shipdate) o_okey o_date o_pri 1 2021.05.01 2 3 2021.05.01 1 5 2021.05.02 1 l_okey l_name l_price l_shipdate 3 shirt 30 2021.05.2 5 lifevest 20 2021.05.02 1 chair 50 2021.05.03 1 monitor 130 2021.05.03 1 desk 100 2021.05.07 1 mouse 10 2021.05.07 5 kayak 200 2021.05.07 o_okey o_date o_pri 2 2021.05.01 1 4 2021.05.02 3 l_okey l_name l_price l_shipdate 2 pot 20 2021.05.01 2 pan 25 2021.05.04 4 bike 120 2021.05.04 4 helmet 30 2021.05.10 Back to Partition Setup Node 1 Node 2 Order Line_Item Line_Item Order
- 22. Examples: Query - Do the partitions help? SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_okey, o_date,o_pri ORDER BY revenue desc, o_date; Yes ● Filter: o_date < 2021.05.02 and l_shipdate > 2021.05.03 ○ → Prune partitions not in the filter ranges
- 23. Examples: Query - Do the partitions help? SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_okey, o_date,o_pri ORDER BY revenue desc, o_date; What if Order is not partitioned on o_date and Line_Item not partitioned on l_shipdate?
- 24. Examples: Query - Do the partitions help? SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_okey, o_date,o_pri ORDER BY revenue desc, o_date; What if Order is not partitioned on o_date and Line_Item not partitioned on l_shipdate? ● → nothing to prune early, we have to scan all column data and apply the filter ranges
- 25. Examples: Query - Do the encoding & sorting help? SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_okey, o_date,o_pri ORDER BY revenue desc, o_date;
- 26. Sharded : Order: (o_okey % 2) & Line_Item: (l_okey % 2) Partitioned : Order: (o_date) & Line_Item: (l_shipdate) Encoded & Sorted : Order: (o_okey) & Line_Item: RLE(l_okey) o_okey o_date o_pri 1 2021.05.01 2 3 2021.05.01 1 5 2021.05.02 1 l_okey l_name l_price l_shipdate (3,1) shirt 30 2021.05.2 (5,1) lifevest 20 2021.05.02 (1, 2) chair 50 2021.05.03 monitor 130 2021.05.03 (1,2) desk 100 2021.05.07 mouse 10 2021.05.07 (5,1) kayak 200 2021.05.07 o_okey o_date o_pri 2 2021.05.01 1 4 2021.05.02 3 l_okey l_name l_price l_shipdate (2,1) pot 20 2021.05.01 (2,1) pan 25 2021.05.04 (4,1) bike 120 2021.05.04 (4,1) helmet 30 2021.05.10 Back to Encoding and Sorting Setup Node 1 Node 2 Order Line_Item Line_Item Order
- 27. Examples: Query - Do the encoding & sorting help? SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_okey, o_date,o_pri ORDER BY revenue desc, o_date; Yes ● Join: l_okey = o_key ○ → use fast & more memory efficient merge join because data already sorted on the join keys ○ → l_okey can be kept in RLE during join ● Group By: l_okey, o_date,o_pri ○ → Group-by key is sorted and no need doing hash groupby, simply group data as we get new batches until we reach higher value
- 28. Examples: Query - Do the encoding & sorting help? SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_okey, o_date,o_pri ORDER BY revenue desc, o_date; What if Order is not sorted on o_okey and Line_Item is not RLE on l_okey?
- 29. Examples: Query - Do the encoding & sorting help? SELECT l_okey, sum(l_price) as revenue, o_date, o_pri FROM customer, orders, lineitem WHERE l_okey = o_key and o_date < 2021.05.02 and l_shipdate > 2021.05.03 GROUP BY l_okey, o_date,o_pri ORDER BY revenue desc, o_date; What if Order is not sorted on o_okey and Line_Item is not RLE on l_okey? ● → use hash join instead (usually slower and requires more memory than merge join) ● → use hash-group-by method (similarly, usually slower and requires more memory than pipe-lined group-by) ● → If there are only a few line items per order, the RLE won’t save much space
- 30. Database Designer: ● Topic for another talk ● Startup: Ottertune https://ottertune.com ○ Database Optimization on Autopilot How to design sharding, partitioning, encoding, and sorting for a combination of queries?
- 31. So what we have demonstrated today? ● Sharding ○ Horizontally split a table into N non-overlapping shards ■ → each node will (equally) share 1/n of the workload: ● Load 1/n data to each node ● Query: join & group-by on each node share 1/n workload ● Partitioning ○ Each shard is further split into smaller partitions for better data filtering, deleting, fanning out, local parallelism ● Encoding ○ Each column is encoded (sorted & compressed) to further help on join, filtering, group-by, order-by → Can you think of examples for the cases we have not covered?
- 32. Thank you