SlideShare a Scribd company logo
1 of 56
Some improvements and practices of
HBase at Xiaomi
Duo Zhang, Liangliang He
{zhangduo, heliangliang}@xiaomi.com
........ ..... ................. ................. ................. .... .... . .... ........ .
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
About Xiaomi
Xiaomi Inc. (literally ”millet technology”) is a privately owned Chinese
electronics company headquartered in Beijing.
▶ Sold 70m+ smart phones in 2015
▶ 100m+ DAU for MIUI
▶ Lots of other smart devices.(Mi Band, Air Purifier, etc.)
2 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Our HDFS/HBase Team
▶ 9 Developers
▶ Honghua Feng
▶ Jianwei Cui
▶ Liangliang He
▶ YingChao Zhou
▶ Guanghao Zhang
▶ Shaohui Liu
▶ Chen Zhang
▶ Zhe Yang
▶ Duo Zhang
3 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Agenda
1. Current Status
2. Problems and Solutions
3. HBase as a service
4 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Clusters and Scenarios
▶ Traditional IDC
20+ online clusters / 2 offline clusters, 3 data centers
▶ AWS
5 online clusters / 1 offline cluster, 3 AWS regions
▶ Online Service
MiCloud, MiPush, SDS, Metrics...
▶ Offline Processing
User Profile, Distributed Trace, Recommendation, ...
5 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Online Scenario: MiCloud
Personal cloud storage for smart phones
Numbers
▶ 100+ million users
▶ 1+ trillion rows
▶ 1600+ regions in the largest table
See: https://i.mi.com
6 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Offline Scenario: User Profile
▶ Input data replicated from online to offline cluster
▶ Output data is written to offline cluster and replicated to online cluster
Numbers
▶ 200+ million users
▶ Both batch and streaming processing
7 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Agenda
1. Current Status
2. Problems and Solutions
3. HBase as a service
8 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Per-CF Flush
HBase book, section 34, On the number of column families:
HBase currently does not do well with anything above two or
three column families ... if one column family is carrying the
bulk of the data bringing on flushes, the adjacent families will
also be flushed even though the amount of data they carry is
small ...
So let’s not flush the small families
9 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Per-CF Flush
HBase book, section 34, On the number of column families:
HBase currently does not do well with anything above two or
three column families ... if one column family is carrying the
bulk of the data bringing on flushes, the adjacent families will
also be flushed even though the amount of data they carry is
small ...
So let’s not flush the small families
9 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Per-CF Flush
▶ Why we must flush all families?
▶ Our sequence id accounting is per region.
▶ Can not know the lowest unflushed sequence id.
▶ Track sequence id per store, i.e., per family
▶ Map<RegionName, SequenceId> to
Map<RegionName, Map<FamilyName, SequenceId>>
▶ SequenceId map in WAL implementation
▶ FlushedSequenceId in ServerManager at master
▶ Report a Map of flushed sequence id to master(Thanks protobuf for
compatibility)
▶ Skip WAL cells per store when replaying
▶ FlushPolicy
▶ FlushAllStoresPolicy
▶ FlushLargeStoresPolicy
10 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Per-CF Flush
▶ Flush is not only used for releasing memory
▶ WAL truncating
▶ Region merge, split, move...
▶ Bulk load
▶ Introduce a ’force’ flag
▶ Always flush all families regardless of which FlushPolicy we use
11 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Per-CF Flush
▶ First introduced in HBase-1.1.x, default is FlushAllStoresPolicy
▶ In HBase-1.2.x, default is FlushLargeStoresPolicy
▶ HBASE-10201, HBASE-12405
12 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Async WAL
13 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Async WAL
Problem: FSHLog
▶ DFSOutputStream is too complicated and hard to optimize
▶ Pipeline recovery
▶ Need to use multiple SyncRunner threads to simulate event-driven.
▶ Chained pipeline, 3 times latency
14 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Async WAL
15 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Async WAL
Solution: AsyncFSWAL and FanOutOneBlockAsyncDFSOutput
▶ Simple, can only write one block
▶ Fail-fast
▶ All things are done in netty’s EventLoop, fully event-driven
▶ Fan out, write to 3 datanodes concurrently
16 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Async WAL
Implementation:
▶ Why not disruptor?
▶ Should not block EventLoop thread
▶ Submit consumer task only if there are entries in queue
▶ Avoid submit a task for every entry
▶ SASL and encryption support
▶ Be compatible with hadoop from 2.4.x to 2.7.x
▶ Classes and methods are changed, moved, removed, etc.
▶ Abstract common interface
▶ Reflection
17 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Async WAL
Performance numbers:
Table: WALPE
Threads Default(s) Async(s) Diff
1 837 228 3.7x
3 647 274 2.4x
5 609 310 2x
10 916 376 2.5x
25 1177 556 2.1x
50 1463 828 1.8x
100 1902 1382 1.4x
▶ Why diff decrease as threads
increase?
▶ high latency ̸= low throughput
▶ Increase concurrency can
increase throughput
▶ The bottle neck is HDD under
high workload
▶ YCSB write qps: roughly 14.3k vs 16.3k, about 10-15% more throughput
18 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Async WAL
▶ Available in HBase-2.0
▶ Also the default WAL implementation in HBase-2.0
▶ Will push the AsyncFSOutput related code to HDFS
▶ HBASE-14790
19 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
Problem: The ’Delete Version’ problem
▶ Let MaxVersion = 2, and timestamp T1 < T2 < T3
1. Put T1, T2, T3
2. Major compaction
3. Delete T2
1. Put T1, T2, T3
2. Delete T2
3. Major compaction
T3 vs. T3, T1
20 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
Problem: The ’Delete Version’ problem
▶ Let MaxVersion = 2, and timestamp T1 < T2 < T3
1. Put T1, T2, T3
2. Major compaction
3. Delete T2
1. Put T1, T2, T3
2. Delete T2
3. Major compaction
T3 vs. T3, T1
20 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
Problem: The ’Delete Version’ problem
▶ Let MaxVersion = 2, and timestamp T1 < T2 < T3
1. Put T1, T2, T3
2. Major compaction
3. Delete T2
1. Put T1, T2, T3
2. Delete T2
3. Major compaction
T3 vs. T3, T1
20 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
Problem: The ’Delete Version’ problem
▶ Let MaxVersion = 2, and timestamp T1 < T2 < T3
1. Put T1, T2, T3
2. Major compaction
3. Delete T2
1. Put T1, T2, T3
2. Delete T2
3. Major compaction
T3 vs. T3, T1
20 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
Problem: The ’Delete Version’ problem
▶ Let MaxVersion = 2, and timestamp T1 < T2 < T3
1. Put T1, T2, T3
2. Major compaction
3. Delete T2
1. Put T1, T2, T3
2. Delete T2
3. Major compaction
T3 vs. T3, T1
20 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
Problem: The ’Delete Version’ problem
▶ Let MaxVersion = 2, and timestamp T1 < T2 < T3
1. Put T1, T2, T3
2. Major compaction
3. Delete T2
1. Put T1, T2, T3
2. Delete T2
3. Major compaction
T3 vs. T3, T1
20 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
Problem: The ’Delete Version’ problem
▶ Let MaxVersion = 2, and timestamp T1 < T2 < T3
1. Put T1, T2, T3
2. Major compaction
3. Delete T2
1. Put T1, T2, T3
2. Delete T2
3. Major compaction
T3 vs. T3, T1
20 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
Problem: The ’Delete Version’ problem
▶ Let MaxVersion = 2, and timestamp T1 < T2 < T3
1. Put T1, T2, T3
2. Major compaction
3. Delete T2
1. Put T1, T2, T3
2. Delete T2
3. Major compaction
T3 vs. T3, T1
20 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
Problem: The ’Delete Version’ problem
▶ Let MaxVersion = 2, and timestamp T1 < T2 < T3
1. Put T1, T2, T3
2. Major compaction
3. Delete T2
1. Put T1, T2, T3
2. Delete T2
3. Major compaction
T3 vs. T3, T1
20 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
Problem: The ’Delete Version’ problem
▶ Let MaxVersion = 2, and timestamp T1 < T2 < T3
1. Put T1, T2, T3
2. Major compaction
3. Delete T2
1. Put T1, T2, T3
2. Delete T2
3. Major compaction
T3 vs. T3, T1
20 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
Problem: Delete has effect on newer Put(with higher sequence id)
▶ Let timestamp T1 < T2
▶ Delete all versions less than T2
▶ Major compaction
▶ Put T1
▶ Delete all versions less than T2
▶ Put T1
▶ Major compaction
T1 vs. Nothing
21 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
Problem: Delete has effect on newer Put(with higher sequence id)
▶ Let timestamp T1 < T2
▶ Delete all versions less than T2
▶ Major compaction
▶ Put T1
▶ Delete all versions less than T2
▶ Put T1
▶ Major compaction
T1 vs. Nothing
21 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
▶ Not a big problem? It depends.
▶ Major compaction is a low frequency operation
▶ You just choose one path so the result is deterministic
▶ What if we use replication?
Eventual inconsistency
22 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
▶ Not a big problem? It depends.
▶ Major compaction is a low frequency operation
▶ You just choose one path so the result is deterministic
▶ What if we use replication?
Eventual inconsistency
22 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
▶ Not a big problem? It depends.
▶ Major compaction is a low frequency operation
▶ You just choose one path so the result is deterministic
▶ What if we use replication?
Eventual inconsistency
22 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
Solution: Also consider sequence id
▶ Once a value is invisible, it should never appear again
▶ A modified scanner that also consider sequence id when deciding visibility
▶ Can not use max timestamp to exclude store files when scan
▶ Delete should not have effect on put with a higher sequence id
▶ Maybe a table level config to turn it on
23 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
▶ Enough?
▶ Not really for replication
▶ The WAL of the same Cell should be sent by ascending order of sequence id
▶ HBASE-2256, HBASE-8721, HBASE-8770...
24 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
▶ Enough?
▶ Not really for replication
▶ The WAL of the same Cell should be sent by ascending order of sequence id
▶ HBASE-2256, HBASE-8721, HBASE-8770...
24 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Revisit the semantic of Delete
▶ Enough?
▶ Not really for replication
▶ The WAL of the same Cell should be sent by ascending order of sequence id
▶ HBASE-2256, HBASE-8721, HBASE-8770...
24 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Multi-Tenancy Practice
Difference from trunk HBase quota implementation
▶ Requests are size weighted when counting quota
▶ Per user instead of per regionserver
▶ Assume the workloads are evenly distributed to each region
▶ Soft qps limit, like DynamoDB
▶ Configurable qps quota limit for each regionserver
▶ User can have a qps higher than its quota if regionserver has free quota
▶ Transparent client side auto backoff when quota exceeds
25 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Cross Data-Center Failover Practice
Modifications of HBase:
▶ HBase nameservice
▶ Read-write switch in client configuration
▶ Dynamic configuration with zookeeper
▶ Record last synced WAL write time when update replication log position
Failover steps:
▶ Check and make sure replication is in-sync
▶ Stop write operation by update config in zookeeper
▶ Check and wait replication is done by checking the sync time of last
replicated log
▶ Switch master cluster and turn on write operation
26 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Cross Data-Center Failover Practice
Modifications of HBase:
▶ HBase nameservice
▶ Read-write switch in client configuration
▶ Dynamic configuration with zookeeper
▶ Record last synced WAL write time when update replication log position
Failover steps:
▶ Check and make sure replication is in-sync
▶ Stop write operation by update config in zookeeper
▶ Check and wait replication is done by checking the sync time of last
replicated log
▶ Switch master cluster and turn on write operation
26 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Cross Data-Center Failover Practice
Modifications of HBase:
▶ HBase nameservice
▶ Read-write switch in client configuration
▶ Dynamic configuration with zookeeper
▶ Record last synced WAL write time when update replication log position
Failover steps:
▶ Check and make sure replication is in-sync
▶ Stop write operation by update config in zookeeper
▶ Check and wait replication is done by checking the sync time of last
replicated log
▶ Switch master cluster and turn on write operation
26 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Cross Data-Center Failover Practice
Modifications of HBase:
▶ HBase nameservice
▶ Read-write switch in client configuration
▶ Dynamic configuration with zookeeper
▶ Record last synced WAL write time when update replication log position
Failover steps:
▶ Check and make sure replication is in-sync
▶ Stop write operation by update config in zookeeper
▶ Check and wait replication is done by checking the sync time of last
replicated log
▶ Switch master cluster and turn on write operation
26 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Agenda
1. Current Status
2. Problems and Solutions
3. HBase as a service
27 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
SDS (Structured Datastore Service)
We build SDS on top of HBase:
▶ Simplified interface, configuration and dependency
▶ Multi-platform support
▶ Flexible access/quota control
▶ Minimized administration cost
28 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Screenshots
29 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Screenshots
30 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Architecture and Applications
Currently serving:
▶ 1000+ tables
▶ Dozens types of smart devices
▶ Several millions of independent devices
31 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
libsds
Formalized Data Model
▶ Entity Group: group of records
belong to a single entity
▶ Primary Index: primary index
within an entity group
▶ Local Secondary Index: index
within a single entity group
▶ Eager index
▶ Lazy index
▶ Immutable index
32 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Example: Schema definition
Example: cloud notebook
-- Equivalent SQL definition
CREATE TABLE note (
userId VARCHAR (64) NOT NULL , -- Entity group key
noteId INT8 NOT NULL , -- Primary key
title VARCHAR (256) ,
body VARCHAR (2048) ,
mtime BIGINT ,
tag VARCHAR (16),
version INT ,
PRIMARY KEY(userId , noteId),
INDEX(userId , mtime),
INDEX(userId , tag)
);
33 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Example: Data Type Definition
@Record(table = "note", family = "B")
public class Note {
@Column(keyOnly = true)
String uid; // user ID
@Column(keyOnly = true)
Long id; // note ID
@Column String title;
@Column(serialization = Column. SerializationType .UNIX_TIME)
private Date mtime;
@Column(collection = true , elementClass = String.class , type =
private Set <String > tags;
@Column(serialization = Column. SerializationType .JSON)
private NoteBody body; 34 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Example: Data Layout
CF Rowkey Values
B hash(userId) userId noteId title, mtime, tags, body, version
I hash(userId) userId idx-mtime mtime noteId title1
I hash(userId) userId idx-tags tag1 noteId title
I hash(userId) userId idx-tags tag2 noteId title
1
projected attribute
35 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Example: Query
// random read
@Override public Note findNoteById(String userId , long nid) {
Note key = new Note(userId , nid , null , null , null , ...);
return typedAccessClient .get(key);
}
// range query , same as SELECT * FROM note
// WHERE uid=userId AND title LIKE ’Test%’
// ORDER BY mtime DESC LIMIT N
@Override public List <ListViewItem > searchNLatestItems (
String userId , int N, String title) {
return typedAccessClient .scan(Note.class ,
ListViewItem .class ,
Constants.IDX_MTIME , // implicitly specify index name
Note.entityGroupNote (userId),
Note.entityGroupNote (userId),
"title␣REGEX␣’" + match + "’",// title REGEX ’Test.*’
N). getRecords (); 36 / 38
.....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
....
.
....
.
.....
.
....
.
.....
.
....
.
....
.
Example: Update
// same as UPDATE note SET version = oldVersion + 1,
// mtime = NOW , contents = ’...’
// WHERE version = oldVersion
// AND uid = userId AND id = noteId
@Override public boolean updateNote(Note note) {
int currentVersion = note.getVersion ();
try {
SimpleCondition versionPredicate =
SimpleCondition .predicate(note.getVersion (),
CompareFilter.CompareOp.EQUAL ,
Constants.VERSION_FIELD );
note.setMtime(new Date ());
note.setVersion( currentVersion + 1);
return typedAccessClient .put(note , versionPredicate );
} finally {
note.setVersion( currentVersion );
}
} 37 / 38
Thanks! Questions?
Contacts: {zhangduo, heliangliang}@xiaomi.com
........ ..... ................. ................. ................. .... .... . .... ........ .

More Related Content

What's hot

Apache HBase Performance Tuning
Apache HBase Performance TuningApache HBase Performance Tuning
Apache HBase Performance TuningLars Hofhansl
 
Time-Series Apache HBase
Time-Series Apache HBaseTime-Series Apache HBase
Time-Series Apache HBaseHBaseCon
 
RocksDB Performance and Reliability Practices
RocksDB Performance and Reliability PracticesRocksDB Performance and Reliability Practices
RocksDB Performance and Reliability PracticesYoshinori Matsunobu
 
Parquet performance tuning: the missing guide
Parquet performance tuning: the missing guideParquet performance tuning: the missing guide
Parquet performance tuning: the missing guideRyan Blue
 
HTTP Analytics for 6M requests per second using ClickHouse, by Alexander Boc...
HTTP Analytics for 6M requests per second using ClickHouse, by  Alexander Boc...HTTP Analytics for 6M requests per second using ClickHouse, by  Alexander Boc...
HTTP Analytics for 6M requests per second using ClickHouse, by Alexander Boc...Altinity Ltd
 
Introduction to Redis
Introduction to RedisIntroduction to Redis
Introduction to RedisDvir Volk
 
Hive + Tez: A Performance Deep Dive
Hive + Tez: A Performance Deep DiveHive + Tez: A Performance Deep Dive
Hive + Tez: A Performance Deep DiveDataWorks Summit
 
PostgreSQL and Benchmarks
PostgreSQL and BenchmarksPostgreSQL and Benchmarks
PostgreSQL and BenchmarksJignesh Shah
 
HBase Advanced - Lars George
HBase Advanced - Lars GeorgeHBase Advanced - Lars George
HBase Advanced - Lars GeorgeJAX London
 
Spark shuffle introduction
Spark shuffle introductionSpark shuffle introduction
Spark shuffle introductioncolorant
 
InfluxDB IOx Tech Talks: Query Engine Design and the Rust-Based DataFusion in...
InfluxDB IOx Tech Talks: Query Engine Design and the Rust-Based DataFusion in...InfluxDB IOx Tech Talks: Query Engine Design and the Rust-Based DataFusion in...
InfluxDB IOx Tech Talks: Query Engine Design and the Rust-Based DataFusion in...InfluxData
 
The Parquet Format and Performance Optimization Opportunities
The Parquet Format and Performance Optimization OpportunitiesThe Parquet Format and Performance Optimization Opportunities
The Parquet Format and Performance Optimization OpportunitiesDatabricks
 
Securing Hadoop with Apache Ranger
Securing Hadoop with Apache RangerSecuring Hadoop with Apache Ranger
Securing Hadoop with Apache RangerDataWorks Summit
 
Improving SparkSQL Performance by 30%: How We Optimize Parquet Pushdown and P...
Improving SparkSQL Performance by 30%: How We Optimize Parquet Pushdown and P...Improving SparkSQL Performance by 30%: How We Optimize Parquet Pushdown and P...
Improving SparkSQL Performance by 30%: How We Optimize Parquet Pushdown and P...Databricks
 
Parquet Hadoop Summit 2013
Parquet Hadoop Summit 2013Parquet Hadoop Summit 2013
Parquet Hadoop Summit 2013Julien Le Dem
 

What's hot (20)

Apache HBase Performance Tuning
Apache HBase Performance TuningApache HBase Performance Tuning
Apache HBase Performance Tuning
 
Time-Series Apache HBase
Time-Series Apache HBaseTime-Series Apache HBase
Time-Series Apache HBase
 
HBase in Practice
HBase in Practice HBase in Practice
HBase in Practice
 
Upgrading HDFS to 3.3.0 and deploying RBF in production #LINE_DM
Upgrading HDFS to 3.3.0 and deploying RBF in production #LINE_DMUpgrading HDFS to 3.3.0 and deploying RBF in production #LINE_DM
Upgrading HDFS to 3.3.0 and deploying RBF in production #LINE_DM
 
Hive tuning
Hive tuningHive tuning
Hive tuning
 
RocksDB Performance and Reliability Practices
RocksDB Performance and Reliability PracticesRocksDB Performance and Reliability Practices
RocksDB Performance and Reliability Practices
 
Parquet performance tuning: the missing guide
Parquet performance tuning: the missing guideParquet performance tuning: the missing guide
Parquet performance tuning: the missing guide
 
HTTP Analytics for 6M requests per second using ClickHouse, by Alexander Boc...
HTTP Analytics for 6M requests per second using ClickHouse, by  Alexander Boc...HTTP Analytics for 6M requests per second using ClickHouse, by  Alexander Boc...
HTTP Analytics for 6M requests per second using ClickHouse, by Alexander Boc...
 
Introduction to Redis
Introduction to RedisIntroduction to Redis
Introduction to Redis
 
Hive + Tez: A Performance Deep Dive
Hive + Tez: A Performance Deep DiveHive + Tez: A Performance Deep Dive
Hive + Tez: A Performance Deep Dive
 
Database storage engines
Database storage enginesDatabase storage engines
Database storage engines
 
PostgreSQL and Benchmarks
PostgreSQL and BenchmarksPostgreSQL and Benchmarks
PostgreSQL and Benchmarks
 
HBase Advanced - Lars George
HBase Advanced - Lars GeorgeHBase Advanced - Lars George
HBase Advanced - Lars George
 
Spark shuffle introduction
Spark shuffle introductionSpark shuffle introduction
Spark shuffle introduction
 
InfluxDB IOx Tech Talks: Query Engine Design and the Rust-Based DataFusion in...
InfluxDB IOx Tech Talks: Query Engine Design and the Rust-Based DataFusion in...InfluxDB IOx Tech Talks: Query Engine Design and the Rust-Based DataFusion in...
InfluxDB IOx Tech Talks: Query Engine Design and the Rust-Based DataFusion in...
 
The Parquet Format and Performance Optimization Opportunities
The Parquet Format and Performance Optimization OpportunitiesThe Parquet Format and Performance Optimization Opportunities
The Parquet Format and Performance Optimization Opportunities
 
Securing Hadoop with Apache Ranger
Securing Hadoop with Apache RangerSecuring Hadoop with Apache Ranger
Securing Hadoop with Apache Ranger
 
Apache Flink Deep Dive
Apache Flink Deep DiveApache Flink Deep Dive
Apache Flink Deep Dive
 
Improving SparkSQL Performance by 30%: How We Optimize Parquet Pushdown and P...
Improving SparkSQL Performance by 30%: How We Optimize Parquet Pushdown and P...Improving SparkSQL Performance by 30%: How We Optimize Parquet Pushdown and P...
Improving SparkSQL Performance by 30%: How We Optimize Parquet Pushdown and P...
 
Parquet Hadoop Summit 2013
Parquet Hadoop Summit 2013Parquet Hadoop Summit 2013
Parquet Hadoop Summit 2013
 

Viewers also liked

Improvements to Apache HBase and Its Applications in Alibaba Search
Improvements to Apache HBase and Its Applications in Alibaba Search Improvements to Apache HBase and Its Applications in Alibaba Search
Improvements to Apache HBase and Its Applications in Alibaba Search HBaseCon
 
Apache HBase at Airbnb
Apache HBase at Airbnb Apache HBase at Airbnb
Apache HBase at Airbnb HBaseCon
 
Keynote: The Future of Apache HBase
Keynote: The Future of Apache HBaseKeynote: The Future of Apache HBase
Keynote: The Future of Apache HBaseHBaseCon
 
Apache HBase - Just the Basics
Apache HBase - Just the BasicsApache HBase - Just the Basics
Apache HBase - Just the BasicsHBaseCon
 
Breaking the Sound Barrier with Persistent Memory
Breaking the Sound Barrier with Persistent Memory Breaking the Sound Barrier with Persistent Memory
Breaking the Sound Barrier with Persistent Memory HBaseCon
 
Apache HBase, Accelerated: In-Memory Flush and Compaction
Apache HBase, Accelerated: In-Memory Flush and Compaction Apache HBase, Accelerated: In-Memory Flush and Compaction
Apache HBase, Accelerated: In-Memory Flush and Compaction HBaseCon
 
Optimizing Apache HBase for Cloud Storage in Microsoft Azure HDInsight
Optimizing Apache HBase for Cloud Storage in Microsoft Azure HDInsightOptimizing Apache HBase for Cloud Storage in Microsoft Azure HDInsight
Optimizing Apache HBase for Cloud Storage in Microsoft Azure HDInsightHBaseCon
 
Apache Spark on Apache HBase: Current and Future
Apache Spark on Apache HBase: Current and Future Apache Spark on Apache HBase: Current and Future
Apache Spark on Apache HBase: Current and Future HBaseCon
 
Argus Production Monitoring at Salesforce
Argus Production Monitoring at SalesforceArgus Production Monitoring at Salesforce
Argus Production Monitoring at SalesforceHBaseCon
 
Rolling Out Apache HBase for Mobile Offerings at Visa
Rolling Out Apache HBase for Mobile Offerings at Visa Rolling Out Apache HBase for Mobile Offerings at Visa
Rolling Out Apache HBase for Mobile Offerings at Visa HBaseCon
 
Solving Multi-tenancy and G1GC in Apache HBase
Solving Multi-tenancy and G1GC in Apache HBase Solving Multi-tenancy and G1GC in Apache HBase
Solving Multi-tenancy and G1GC in Apache HBase HBaseCon
 
HBaseCon 2015: Solving HBase Performance Problems with Apache HTrace
HBaseCon 2015: Solving HBase Performance Problems with Apache HTraceHBaseCon 2015: Solving HBase Performance Problems with Apache HTrace
HBaseCon 2015: Solving HBase Performance Problems with Apache HTraceHBaseCon
 
HBase Data Modeling and Access Patterns with Kite SDK
HBase Data Modeling and Access Patterns with Kite SDKHBase Data Modeling and Access Patterns with Kite SDK
HBase Data Modeling and Access Patterns with Kite SDKHBaseCon
 
HBaseCon 2015: Graph Processing of Stock Market Order Flow in HBase on AWS
HBaseCon 2015: Graph Processing of Stock Market Order Flow in HBase on AWSHBaseCon 2015: Graph Processing of Stock Market Order Flow in HBase on AWS
HBaseCon 2015: Graph Processing of Stock Market Order Flow in HBase on AWSHBaseCon
 
Apache Kylin’s Performance Boost from Apache HBase
Apache Kylin’s Performance Boost from Apache HBaseApache Kylin’s Performance Boost from Apache HBase
Apache Kylin’s Performance Boost from Apache HBaseHBaseCon
 
Update on OpenTSDB and AsyncHBase
Update on OpenTSDB and AsyncHBase Update on OpenTSDB and AsyncHBase
Update on OpenTSDB and AsyncHBase HBaseCon
 
HBaseCon 2015: S2Graph - A Large-scale Graph Database with HBase
HBaseCon 2015: S2Graph - A Large-scale Graph Database with HBaseHBaseCon 2015: S2Graph - A Large-scale Graph Database with HBase
HBaseCon 2015: S2Graph - A Large-scale Graph Database with HBaseHBaseCon
 
Apache Phoenix: Use Cases and New Features
Apache Phoenix: Use Cases and New FeaturesApache Phoenix: Use Cases and New Features
Apache Phoenix: Use Cases and New FeaturesHBaseCon
 
Update on OpenTSDB and AsyncHBase
Update on OpenTSDB and AsyncHBase Update on OpenTSDB and AsyncHBase
Update on OpenTSDB and AsyncHBase HBaseCon
 
HBaseCon 2015 General Session: State of HBase
HBaseCon 2015 General Session: State of HBaseHBaseCon 2015 General Session: State of HBase
HBaseCon 2015 General Session: State of HBaseHBaseCon
 

Viewers also liked (20)

Improvements to Apache HBase and Its Applications in Alibaba Search
Improvements to Apache HBase and Its Applications in Alibaba Search Improvements to Apache HBase and Its Applications in Alibaba Search
Improvements to Apache HBase and Its Applications in Alibaba Search
 
Apache HBase at Airbnb
Apache HBase at Airbnb Apache HBase at Airbnb
Apache HBase at Airbnb
 
Keynote: The Future of Apache HBase
Keynote: The Future of Apache HBaseKeynote: The Future of Apache HBase
Keynote: The Future of Apache HBase
 
Apache HBase - Just the Basics
Apache HBase - Just the BasicsApache HBase - Just the Basics
Apache HBase - Just the Basics
 
Breaking the Sound Barrier with Persistent Memory
Breaking the Sound Barrier with Persistent Memory Breaking the Sound Barrier with Persistent Memory
Breaking the Sound Barrier with Persistent Memory
 
Apache HBase, Accelerated: In-Memory Flush and Compaction
Apache HBase, Accelerated: In-Memory Flush and Compaction Apache HBase, Accelerated: In-Memory Flush and Compaction
Apache HBase, Accelerated: In-Memory Flush and Compaction
 
Optimizing Apache HBase for Cloud Storage in Microsoft Azure HDInsight
Optimizing Apache HBase for Cloud Storage in Microsoft Azure HDInsightOptimizing Apache HBase for Cloud Storage in Microsoft Azure HDInsight
Optimizing Apache HBase for Cloud Storage in Microsoft Azure HDInsight
 
Apache Spark on Apache HBase: Current and Future
Apache Spark on Apache HBase: Current and Future Apache Spark on Apache HBase: Current and Future
Apache Spark on Apache HBase: Current and Future
 
Argus Production Monitoring at Salesforce
Argus Production Monitoring at SalesforceArgus Production Monitoring at Salesforce
Argus Production Monitoring at Salesforce
 
Rolling Out Apache HBase for Mobile Offerings at Visa
Rolling Out Apache HBase for Mobile Offerings at Visa Rolling Out Apache HBase for Mobile Offerings at Visa
Rolling Out Apache HBase for Mobile Offerings at Visa
 
Solving Multi-tenancy and G1GC in Apache HBase
Solving Multi-tenancy and G1GC in Apache HBase Solving Multi-tenancy and G1GC in Apache HBase
Solving Multi-tenancy and G1GC in Apache HBase
 
HBaseCon 2015: Solving HBase Performance Problems with Apache HTrace
HBaseCon 2015: Solving HBase Performance Problems with Apache HTraceHBaseCon 2015: Solving HBase Performance Problems with Apache HTrace
HBaseCon 2015: Solving HBase Performance Problems with Apache HTrace
 
HBase Data Modeling and Access Patterns with Kite SDK
HBase Data Modeling and Access Patterns with Kite SDKHBase Data Modeling and Access Patterns with Kite SDK
HBase Data Modeling and Access Patterns with Kite SDK
 
HBaseCon 2015: Graph Processing of Stock Market Order Flow in HBase on AWS
HBaseCon 2015: Graph Processing of Stock Market Order Flow in HBase on AWSHBaseCon 2015: Graph Processing of Stock Market Order Flow in HBase on AWS
HBaseCon 2015: Graph Processing of Stock Market Order Flow in HBase on AWS
 
Apache Kylin’s Performance Boost from Apache HBase
Apache Kylin’s Performance Boost from Apache HBaseApache Kylin’s Performance Boost from Apache HBase
Apache Kylin’s Performance Boost from Apache HBase
 
Update on OpenTSDB and AsyncHBase
Update on OpenTSDB and AsyncHBase Update on OpenTSDB and AsyncHBase
Update on OpenTSDB and AsyncHBase
 
HBaseCon 2015: S2Graph - A Large-scale Graph Database with HBase
HBaseCon 2015: S2Graph - A Large-scale Graph Database with HBaseHBaseCon 2015: S2Graph - A Large-scale Graph Database with HBase
HBaseCon 2015: S2Graph - A Large-scale Graph Database with HBase
 
Apache Phoenix: Use Cases and New Features
Apache Phoenix: Use Cases and New FeaturesApache Phoenix: Use Cases and New Features
Apache Phoenix: Use Cases and New Features
 
Update on OpenTSDB and AsyncHBase
Update on OpenTSDB and AsyncHBase Update on OpenTSDB and AsyncHBase
Update on OpenTSDB and AsyncHBase
 
HBaseCon 2015 General Session: State of HBase
HBaseCon 2015 General Session: State of HBaseHBaseCon 2015 General Session: State of HBase
HBaseCon 2015 General Session: State of HBase
 

Similar to Apache HBase Improvements and Practices at Xiaomi

Deployment guide
Deployment guideDeployment guide
Deployment guidedonzerci
 
Motorola ws2000 wireless switch cli reference guide
Motorola ws2000 wireless switch cli reference guideMotorola ws2000 wireless switch cli reference guide
Motorola ws2000 wireless switch cli reference guideAdvantec Distribution
 
Motorola ws2000 wireless switch cli reference guide
Motorola ws2000 wireless switch cli reference guideMotorola ws2000 wireless switch cli reference guide
Motorola ws2000 wireless switch cli reference guideAdvantec Distribution
 
T Series Core Router Architecture Review (Whitepaper)
T Series Core Router Architecture Review (Whitepaper)T Series Core Router Architecture Review (Whitepaper)
T Series Core Router Architecture Review (Whitepaper)Juniper Networks
 
Gigaset SL910A Digital Cordless Telephone User Guide
Gigaset SL910A Digital Cordless Telephone User GuideGigaset SL910A Digital Cordless Telephone User Guide
Gigaset SL910A Digital Cordless Telephone User GuideTelephones Online
 
Burst TCP: an approach for benefiting mice flows
Burst TCP: an approach for benefiting mice flowsBurst TCP: an approach for benefiting mice flows
Burst TCP: an approach for benefiting mice flowsGlauco Gonçalves
 
Tcxd 300 manual_02.10.10
Tcxd 300 manual_02.10.10Tcxd 300 manual_02.10.10
Tcxd 300 manual_02.10.10jftorresco
 
Beej Guide Network Programming
Beej Guide Network ProgrammingBeej Guide Network Programming
Beej Guide Network ProgrammingSriram Raj
 
@author Jane Programmer @cwid 123 45 678 @class
   @author Jane Programmer  @cwid   123 45 678  @class   @author Jane Programmer  @cwid   123 45 678  @class
@author Jane Programmer @cwid 123 45 678 @classtroutmanboris
 
Gigaset S820A Digital Cordless Telephone User Guide
Gigaset S820A Digital Cordless Telephone User GuideGigaset S820A Digital Cordless Telephone User Guide
Gigaset S820A Digital Cordless Telephone User GuideTelephones Online
 
@author Jane Programmer @cwid 123 45 678 @class.docx
   @author Jane Programmer  @cwid   123 45 678  @class.docx   @author Jane Programmer  @cwid   123 45 678  @class.docx
@author Jane Programmer @cwid 123 45 678 @class.docxShiraPrater50
 
Gigaset C620A Digital Cordless Telephone User Guide
Gigaset C620A Digital Cordless Telephone User GuideGigaset C620A Digital Cordless Telephone User Guide
Gigaset C620A Digital Cordless Telephone User GuideTelephones Online
 
Reverse engineering for_beginners-en
Reverse engineering for_beginners-enReverse engineering for_beginners-en
Reverse engineering for_beginners-enAndri Yabu
 
Spm6600 Dx6i Manual Lo Res
Spm6600 Dx6i Manual Lo ResSpm6600 Dx6i Manual Lo Res
Spm6600 Dx6i Manual Lo Resguestada3c8
 

Similar to Apache HBase Improvements and Practices at Xiaomi (20)

Deployment guide
Deployment guideDeployment guide
Deployment guide
 
Motorola ws2000 wireless switch cli reference guide
Motorola ws2000 wireless switch cli reference guideMotorola ws2000 wireless switch cli reference guide
Motorola ws2000 wireless switch cli reference guide
 
Motorola ws2000 wireless switch cli reference guide
Motorola ws2000 wireless switch cli reference guideMotorola ws2000 wireless switch cli reference guide
Motorola ws2000 wireless switch cli reference guide
 
T Series Core Router Architecture Review (Whitepaper)
T Series Core Router Architecture Review (Whitepaper)T Series Core Router Architecture Review (Whitepaper)
T Series Core Router Architecture Review (Whitepaper)
 
Gigaset SL910A Digital Cordless Telephone User Guide
Gigaset SL910A Digital Cordless Telephone User GuideGigaset SL910A Digital Cordless Telephone User Guide
Gigaset SL910A Digital Cordless Telephone User Guide
 
Burst TCP: an approach for benefiting mice flows
Burst TCP: an approach for benefiting mice flowsBurst TCP: an approach for benefiting mice flows
Burst TCP: an approach for benefiting mice flows
 
Tcxd 300 manual_02.10.10
Tcxd 300 manual_02.10.10Tcxd 300 manual_02.10.10
Tcxd 300 manual_02.10.10
 
Beej Guide Network Programming
Beej Guide Network ProgrammingBeej Guide Network Programming
Beej Guide Network Programming
 
refman
refmanrefman
refman
 
Jakarta strutslive
Jakarta strutsliveJakarta strutslive
Jakarta strutslive
 
Jakarta struts
Jakarta strutsJakarta struts
Jakarta struts
 
Struts Live
Struts LiveStruts Live
Struts Live
 
Novell login documentation and troubleshooting
Novell login documentation and troubleshootingNovell login documentation and troubleshooting
Novell login documentation and troubleshooting
 
@author Jane Programmer @cwid 123 45 678 @class
   @author Jane Programmer  @cwid   123 45 678  @class   @author Jane Programmer  @cwid   123 45 678  @class
@author Jane Programmer @cwid 123 45 678 @class
 
Gigaset S820A Digital Cordless Telephone User Guide
Gigaset S820A Digital Cordless Telephone User GuideGigaset S820A Digital Cordless Telephone User Guide
Gigaset S820A Digital Cordless Telephone User Guide
 
@author Jane Programmer @cwid 123 45 678 @class.docx
   @author Jane Programmer  @cwid   123 45 678  @class.docx   @author Jane Programmer  @cwid   123 45 678  @class.docx
@author Jane Programmer @cwid 123 45 678 @class.docx
 
Gigaset C620A Digital Cordless Telephone User Guide
Gigaset C620A Digital Cordless Telephone User GuideGigaset C620A Digital Cordless Telephone User Guide
Gigaset C620A Digital Cordless Telephone User Guide
 
Dpl
DplDpl
Dpl
 
Reverse engineering for_beginners-en
Reverse engineering for_beginners-enReverse engineering for_beginners-en
Reverse engineering for_beginners-en
 
Spm6600 Dx6i Manual Lo Res
Spm6600 Dx6i Manual Lo ResSpm6600 Dx6i Manual Lo Res
Spm6600 Dx6i Manual Lo Res
 

More from HBaseCon

hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes
hbaseconasia2017: Building online HBase cluster of Zhihu based on Kuberneteshbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes
hbaseconasia2017: Building online HBase cluster of Zhihu based on KubernetesHBaseCon
 
hbaseconasia2017: HBase on Beam
hbaseconasia2017: HBase on Beamhbaseconasia2017: HBase on Beam
hbaseconasia2017: HBase on BeamHBaseCon
 
hbaseconasia2017: HBase Disaster Recovery Solution at Huawei
hbaseconasia2017: HBase Disaster Recovery Solution at Huaweihbaseconasia2017: HBase Disaster Recovery Solution at Huawei
hbaseconasia2017: HBase Disaster Recovery Solution at HuaweiHBaseCon
 
hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest
hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinteresthbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest
hbaseconasia2017: Removable singularity: a story of HBase upgrade in PinterestHBaseCon
 
hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程
hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程
hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程HBaseCon
 
hbaseconasia2017: Apache HBase at Netease
hbaseconasia2017: Apache HBase at Neteasehbaseconasia2017: Apache HBase at Netease
hbaseconasia2017: Apache HBase at NeteaseHBaseCon
 
hbaseconasia2017: HBase在Hulu的使用和实践
hbaseconasia2017: HBase在Hulu的使用和实践hbaseconasia2017: HBase在Hulu的使用和实践
hbaseconasia2017: HBase在Hulu的使用和实践HBaseCon
 
hbaseconasia2017: 基于HBase的企业级大数据平台
hbaseconasia2017: 基于HBase的企业级大数据平台hbaseconasia2017: 基于HBase的企业级大数据平台
hbaseconasia2017: 基于HBase的企业级大数据平台HBaseCon
 
hbaseconasia2017: HBase at JD.com
hbaseconasia2017: HBase at JD.comhbaseconasia2017: HBase at JD.com
hbaseconasia2017: HBase at JD.comHBaseCon
 
hbaseconasia2017: Large scale data near-line loading method and architecture
hbaseconasia2017: Large scale data near-line loading method and architecturehbaseconasia2017: Large scale data near-line loading method and architecture
hbaseconasia2017: Large scale data near-line loading method and architectureHBaseCon
 
hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei
hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huaweihbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei
hbaseconasia2017: Ecosystems with HBase and CloudTable service at HuaweiHBaseCon
 
hbaseconasia2017: HBase Practice At XiaoMi
hbaseconasia2017: HBase Practice At XiaoMihbaseconasia2017: HBase Practice At XiaoMi
hbaseconasia2017: HBase Practice At XiaoMiHBaseCon
 
hbaseconasia2017: hbase-2.0.0
hbaseconasia2017: hbase-2.0.0hbaseconasia2017: hbase-2.0.0
hbaseconasia2017: hbase-2.0.0HBaseCon
 
HBaseCon2017 Democratizing HBase
HBaseCon2017 Democratizing HBaseHBaseCon2017 Democratizing HBase
HBaseCon2017 Democratizing HBaseHBaseCon
 
HBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest
HBaseCon2017 Removable singularity: a story of HBase upgrade in PinterestHBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest
HBaseCon2017 Removable singularity: a story of HBase upgrade in PinterestHBaseCon
 
HBaseCon2017 Quanta: Quora's hierarchical counting system on HBase
HBaseCon2017 Quanta: Quora's hierarchical counting system on HBaseHBaseCon2017 Quanta: Quora's hierarchical counting system on HBase
HBaseCon2017 Quanta: Quora's hierarchical counting system on HBaseHBaseCon
 
HBaseCon2017 Transactions in HBase
HBaseCon2017 Transactions in HBaseHBaseCon2017 Transactions in HBase
HBaseCon2017 Transactions in HBaseHBaseCon
 
HBaseCon2017 Highly-Available HBase
HBaseCon2017 Highly-Available HBaseHBaseCon2017 Highly-Available HBase
HBaseCon2017 Highly-Available HBaseHBaseCon
 
HBaseCon2017 Apache HBase at Didi
HBaseCon2017 Apache HBase at DidiHBaseCon2017 Apache HBase at Didi
HBaseCon2017 Apache HBase at DidiHBaseCon
 
HBaseCon2017 gohbase: Pure Go HBase Client
HBaseCon2017 gohbase: Pure Go HBase ClientHBaseCon2017 gohbase: Pure Go HBase Client
HBaseCon2017 gohbase: Pure Go HBase ClientHBaseCon
 

More from HBaseCon (20)

hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes
hbaseconasia2017: Building online HBase cluster of Zhihu based on Kuberneteshbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes
hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes
 
hbaseconasia2017: HBase on Beam
hbaseconasia2017: HBase on Beamhbaseconasia2017: HBase on Beam
hbaseconasia2017: HBase on Beam
 
hbaseconasia2017: HBase Disaster Recovery Solution at Huawei
hbaseconasia2017: HBase Disaster Recovery Solution at Huaweihbaseconasia2017: HBase Disaster Recovery Solution at Huawei
hbaseconasia2017: HBase Disaster Recovery Solution at Huawei
 
hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest
hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinteresthbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest
hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest
 
hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程
hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程
hbaseconasia2017: HareQL:快速HBase查詢工具的發展過程
 
hbaseconasia2017: Apache HBase at Netease
hbaseconasia2017: Apache HBase at Neteasehbaseconasia2017: Apache HBase at Netease
hbaseconasia2017: Apache HBase at Netease
 
hbaseconasia2017: HBase在Hulu的使用和实践
hbaseconasia2017: HBase在Hulu的使用和实践hbaseconasia2017: HBase在Hulu的使用和实践
hbaseconasia2017: HBase在Hulu的使用和实践
 
hbaseconasia2017: 基于HBase的企业级大数据平台
hbaseconasia2017: 基于HBase的企业级大数据平台hbaseconasia2017: 基于HBase的企业级大数据平台
hbaseconasia2017: 基于HBase的企业级大数据平台
 
hbaseconasia2017: HBase at JD.com
hbaseconasia2017: HBase at JD.comhbaseconasia2017: HBase at JD.com
hbaseconasia2017: HBase at JD.com
 
hbaseconasia2017: Large scale data near-line loading method and architecture
hbaseconasia2017: Large scale data near-line loading method and architecturehbaseconasia2017: Large scale data near-line loading method and architecture
hbaseconasia2017: Large scale data near-line loading method and architecture
 
hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei
hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huaweihbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei
hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei
 
hbaseconasia2017: HBase Practice At XiaoMi
hbaseconasia2017: HBase Practice At XiaoMihbaseconasia2017: HBase Practice At XiaoMi
hbaseconasia2017: HBase Practice At XiaoMi
 
hbaseconasia2017: hbase-2.0.0
hbaseconasia2017: hbase-2.0.0hbaseconasia2017: hbase-2.0.0
hbaseconasia2017: hbase-2.0.0
 
HBaseCon2017 Democratizing HBase
HBaseCon2017 Democratizing HBaseHBaseCon2017 Democratizing HBase
HBaseCon2017 Democratizing HBase
 
HBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest
HBaseCon2017 Removable singularity: a story of HBase upgrade in PinterestHBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest
HBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest
 
HBaseCon2017 Quanta: Quora's hierarchical counting system on HBase
HBaseCon2017 Quanta: Quora's hierarchical counting system on HBaseHBaseCon2017 Quanta: Quora's hierarchical counting system on HBase
HBaseCon2017 Quanta: Quora's hierarchical counting system on HBase
 
HBaseCon2017 Transactions in HBase
HBaseCon2017 Transactions in HBaseHBaseCon2017 Transactions in HBase
HBaseCon2017 Transactions in HBase
 
HBaseCon2017 Highly-Available HBase
HBaseCon2017 Highly-Available HBaseHBaseCon2017 Highly-Available HBase
HBaseCon2017 Highly-Available HBase
 
HBaseCon2017 Apache HBase at Didi
HBaseCon2017 Apache HBase at DidiHBaseCon2017 Apache HBase at Didi
HBaseCon2017 Apache HBase at Didi
 
HBaseCon2017 gohbase: Pure Go HBase Client
HBaseCon2017 gohbase: Pure Go HBase ClientHBaseCon2017 gohbase: Pure Go HBase Client
HBaseCon2017 gohbase: Pure Go HBase Client
 

Recently uploaded

Sending Calendar Invites on SES and Calendarsnack.pdf
Sending Calendar Invites on SES and Calendarsnack.pdfSending Calendar Invites on SES and Calendarsnack.pdf
Sending Calendar Invites on SES and Calendarsnack.pdf31events.com
 
Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...
Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...
Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...Natan Silnitsky
 
Call Us🔝>༒+91-9711147426⇛Call In girls karol bagh (Delhi)
Call Us🔝>༒+91-9711147426⇛Call In girls karol bagh (Delhi)Call Us🔝>༒+91-9711147426⇛Call In girls karol bagh (Delhi)
Call Us🔝>༒+91-9711147426⇛Call In girls karol bagh (Delhi)jennyeacort
 
Simplifying Microservices & Apps - The art of effortless development - Meetup...
Simplifying Microservices & Apps - The art of effortless development - Meetup...Simplifying Microservices & Apps - The art of effortless development - Meetup...
Simplifying Microservices & Apps - The art of effortless development - Meetup...Rob Geurden
 
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsSensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsChristian Birchler
 
What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...Technogeeks
 
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...Cizo Technology Services
 
Patterns for automating API delivery. API conference
Patterns for automating API delivery. API conferencePatterns for automating API delivery. API conference
Patterns for automating API delivery. API conferencessuser9e7c64
 
Precise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive GoalPrecise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive GoalLionel Briand
 
Unveiling the Future: Sylius 2.0 New Features
Unveiling the Future: Sylius 2.0 New FeaturesUnveiling the Future: Sylius 2.0 New Features
Unveiling the Future: Sylius 2.0 New FeaturesŁukasz Chruściel
 
Large Language Models for Test Case Evolution and Repair
Large Language Models for Test Case Evolution and RepairLarge Language Models for Test Case Evolution and Repair
Large Language Models for Test Case Evolution and RepairLionel Briand
 
Post Quantum Cryptography – The Impact on Identity
Post Quantum Cryptography – The Impact on IdentityPost Quantum Cryptography – The Impact on Identity
Post Quantum Cryptography – The Impact on Identityteam-WIBU
 
Exploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdf
Exploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdfExploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdf
Exploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdfkalichargn70th171
 
Ronisha Informatics Private Limited Catalogue
Ronisha Informatics Private Limited CatalogueRonisha Informatics Private Limited Catalogue
Ronisha Informatics Private Limited Catalogueitservices996
 
英国UN学位证,北安普顿大学毕业证书1:1制作
英国UN学位证,北安普顿大学毕业证书1:1制作英国UN学位证,北安普顿大学毕业证书1:1制作
英国UN学位证,北安普顿大学毕业证书1:1制作qr0udbr0
 
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte GermanySuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte GermanyChristoph Pohl
 
OpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full Recording
OpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full RecordingOpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full Recording
OpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full RecordingShane Coughlan
 
Understanding Flamingo - DeepMind's VLM Architecture
Understanding Flamingo - DeepMind's VLM ArchitectureUnderstanding Flamingo - DeepMind's VLM Architecture
Understanding Flamingo - DeepMind's VLM Architecturerahul_net
 
Machine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringMachine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringHironori Washizaki
 
VK Business Profile - provides IT solutions and Web Development
VK Business Profile - provides IT solutions and Web DevelopmentVK Business Profile - provides IT solutions and Web Development
VK Business Profile - provides IT solutions and Web Developmentvyaparkranti
 

Recently uploaded (20)

Sending Calendar Invites on SES and Calendarsnack.pdf
Sending Calendar Invites on SES and Calendarsnack.pdfSending Calendar Invites on SES and Calendarsnack.pdf
Sending Calendar Invites on SES and Calendarsnack.pdf
 
Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...
Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...
Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...
 
Call Us🔝>༒+91-9711147426⇛Call In girls karol bagh (Delhi)
Call Us🔝>༒+91-9711147426⇛Call In girls karol bagh (Delhi)Call Us🔝>༒+91-9711147426⇛Call In girls karol bagh (Delhi)
Call Us🔝>༒+91-9711147426⇛Call In girls karol bagh (Delhi)
 
Simplifying Microservices & Apps - The art of effortless development - Meetup...
Simplifying Microservices & Apps - The art of effortless development - Meetup...Simplifying Microservices & Apps - The art of effortless development - Meetup...
Simplifying Microservices & Apps - The art of effortless development - Meetup...
 
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsSensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
 
What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...
 
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
 
Patterns for automating API delivery. API conference
Patterns for automating API delivery. API conferencePatterns for automating API delivery. API conference
Patterns for automating API delivery. API conference
 
Precise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive GoalPrecise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive Goal
 
Unveiling the Future: Sylius 2.0 New Features
Unveiling the Future: Sylius 2.0 New FeaturesUnveiling the Future: Sylius 2.0 New Features
Unveiling the Future: Sylius 2.0 New Features
 
Large Language Models for Test Case Evolution and Repair
Large Language Models for Test Case Evolution and RepairLarge Language Models for Test Case Evolution and Repair
Large Language Models for Test Case Evolution and Repair
 
Post Quantum Cryptography – The Impact on Identity
Post Quantum Cryptography – The Impact on IdentityPost Quantum Cryptography – The Impact on Identity
Post Quantum Cryptography – The Impact on Identity
 
Exploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdf
Exploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdfExploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdf
Exploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdf
 
Ronisha Informatics Private Limited Catalogue
Ronisha Informatics Private Limited CatalogueRonisha Informatics Private Limited Catalogue
Ronisha Informatics Private Limited Catalogue
 
英国UN学位证,北安普顿大学毕业证书1:1制作
英国UN学位证,北安普顿大学毕业证书1:1制作英国UN学位证,北安普顿大学毕业证书1:1制作
英国UN学位证,北安普顿大学毕业证书1:1制作
 
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte GermanySuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
 
OpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full Recording
OpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full RecordingOpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full Recording
OpenChain AI Study Group - Europe and Asia Recap - 2024-04-11 - Full Recording
 
Understanding Flamingo - DeepMind's VLM Architecture
Understanding Flamingo - DeepMind's VLM ArchitectureUnderstanding Flamingo - DeepMind's VLM Architecture
Understanding Flamingo - DeepMind's VLM Architecture
 
Machine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringMachine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their Engineering
 
VK Business Profile - provides IT solutions and Web Development
VK Business Profile - provides IT solutions and Web DevelopmentVK Business Profile - provides IT solutions and Web Development
VK Business Profile - provides IT solutions and Web Development
 

Apache HBase Improvements and Practices at Xiaomi