Improving HDFS Availability with IPC Quality of Service

Improving HDFS Availability with
Hadoop RPC Quality of Service
Hadoop Summit 2015

• Hadoop performance at scale
Ming Ma
• Hadoop reliability and scalability
Twitter Hadoop Team
Chris Li
Data Platform
Who We Are

@twitterhadoop
Agenda
‣Diagnosis of Namenode Congestion
• How does QoS help?
• How to use QoS in your clusters

@twitterhadoop
Hadoop Workloads @ Twitter, ebay
• Large scale
• Thousands of machines
• Tens of thousands of jobs / day
• Diverse
• Production vs ad-hoc
• Batch vs interactive vs iterative
• Require performance isolation

@twitterhadoop
Solutions for Performance Isolation
• YARN: ﬂexible cluster resource management
• Cross Data Center Trafﬁc QoS
• Set QoS policy via DSCP bits in IP header
• HDFS Federation
• Cluster Separation: run high SLA jobs in another
cluster

@twitterhadoop
Unsolved Extreme Cluster Slowdown

@twitterhadoop
• hadoop fs -ls takes 5+ seconds

@twitterhadoop
• hadoop fs -ls takes 5+ seconds
• Worst case: cluster outage
• Namenode lost some datanode heartbeats → replication storm

@twitterhadoop
Audit Logs to the Rescue

@twitterhadoop
• Username, operation type, date record logged for
each operation

@twitterhadoop
• Username, operation type, date record logged for
each operation
• We automatically backup into HDFS

@twitterhadoop
(Hadoop Learning about Itself)

@twitterhadoop
Cause: Resource Monopolization
Each color is a
different user
Area is number of calls

@twitterhadoop
What’s wrong with this code?
while (true) {
fileSystem.exists("/foo");
}

@twitterhadoop
while (true) {
}
Don’t do this at home

@twitterhadoop
while (true) {
}
Don’t do this at home
Unless QoS is on ;)

@twitterhadoop
Bad Code + MapReduce
= DDoS on Namenode!
Namenode
Bad User
Good Users
Other Users

@twitterhadoop
Client Process Namenode Process
RPC Server
RPC Client
DFS Client Namenode Service
Responders
NN Lock
Hadoop RPC Overview
FIFO Call Queue HandlersReaders

@twitterhadoop
Hadoop RPC Overview
FIFO Call Queue HandlersReaders

@twitterhadoop
Diagnosing Congestion
FIFO Call Queue
HandlersReaders

@twitterhadoop
Good User
Bad User
FIFO Call Queue
HandlersReaders

@twitterhadoop
HandlersReaders
Good User
Bad User

@twitterhadoop
HandlersReaders
Good User
Bad User
••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••

@twitterhadoop
Solutions we’ve considered

@twitterhadoop
• HDFS Federation

@twitterhadoop
• HDFS Federation
• Use separate RPC server for datanode requests
(service RPC)

@twitterhadoop
• HDFS Federation
• Use separate RPC server for datanode requests
(service RPC)
• Namenode global lock

@twitterhadoop
Agenda
✓ Diagnosis of Namenode Congestion
‣How does QoS help?
• How to use QoS in your clusters

@twitterhadoop
Goals
• Achieve Fairness and QoS
• No performance degradation
• High throughput
• Low overhead

@twitterhadoop
Model it as a scheduling problem
• Available resource is the RPC handler thread
• Users should be given a fair share of resources

@twitterhadoop
Design Considerations
• Pluggable, conﬁgurable
• Simplifying assumptions:
• All users are equal
• All RPC calls have the same cost
• Leverage existing scheduling algorithms

@twitterhadoop
Solving Congestion with FairCallQueue
Call Queue
HandlersReaders
Good User
Bad User
Queue 0
Queue 1
Queue 2
Queue 3Scheduler
Multiplexer

@twitterhadoop
Fair Scheduling
Call Queue
HandlersReaders
Good User
Bad User

@twitterhadoop
Fair Scheduling: Good User
Call Queue
HandlersReaders
Good User
Bad User

@twitterhadoop
Call Queue
HandlersReaders
Good User
Bad User
11%

@twitterhadoop
Call Queue
HandlersReaders
Good User
Bad User
Queue 0: < 12%

@twitterhadoop
Fair Scheduling: Bad User
Call Queue
HandlersReaders
Good User
Bad User

@twitterhadoop
Call Queue
HandlersReaders
Good User
Bad User
80%

@twitterhadoop
Call Queue
HandlersReaders
Good User
Bad User
Queue 3: > 50%

@twitterhadoop
Fair Scheduling Result
Call Queue
HandlersReaders
Good User
Bad User

@twitterhadoop
Weighted Round-Robin Multiplexing
Call Queue
HandlersReaders
Good User
Bad User

@twitterhadoop
Call Queue
HandlersReaders
Good User
Bad User
Take 3

@twitterhadoop
Call Queue
HandlersReaders
Good User
Bad User
Take 2

@twitterhadoop
Call Queue
HandlersReaders
Good User
Bad User
Repeat

@twitterhadoop
FairCallQueue preventing high latency
FIFO CallQueue
FairCallQueue

@twitterhadoop
RPC Backoff
• Prevents RPC queue from completely ﬁlling up
• Clients are told to wait and retry with exponential
backoff

RPC Backoff
Good User
Bad User
Call Queue
HandlersReaders
Good User

RPC Backoff
Good User
Bad User
Call Queue
HandlersReaders
Good User
RetriableException

@twitterhadoop
RPC Backoff Effects
ConnectTimeoutException
ConnectTimeoutException
GoodAppLatency(ms)
0
2250
4500
6750
9000
Abusive App - number of clients - number of connections
100 x 100 1k x 1k 10k x 100 10k x 500 10k x 10k 50k x 50k
Normal FairCallQueue FairCallQueue + RPC Backoff

@twitterhadoop
Current Status
• Enabled on all Twitter and ebay production
clusters for 6+ months
• Open source availability: HADOOP-9640
• Swappable call queue in 2.4
• FairCallQueue in 2.6
• RPC Backoff in 2.8

@twitterhadoop
Agenda
✓ Diagnosis of Namenode Congestion
✓ How does QoS help?
‣How to use QoS in your clusters

@twitterhadoop
QoS is Easy to Enable
hdfs-site.xml:
<property>
<name>ipc.8020.callqueue.impl</name>
<value>org.apache.hadoop.ipc.FairCallQueue</value>
</property>
<property>
<name>ipc.8020.backoff.enable</name>
<value>true</value>
</property>
Port you want QoS on

@twitterhadoop
Future Possibilities
• RPC scheduling improvements
• Weighted share per user
• Prioritize datanode RPCs over client RPC
• Overall HDFS QoS
• Namenode ﬁne-grained locking
• Fairness for data transfers
• HTTP based payloads such as webHDFS

@twitterhadoop
Conclusion
• Try it out!
• No more namenode congestion since it’s been
enabled at both Twitter and ebay
• Providing QoS at the RPC level is an important
step towards HDFS ﬁne-grained QoS

@twitterhadoop
Special thanks to our reviewers:
• Arpit Agarwal (Hortonworks)
• Daryn Sharp (Yahoo)
• Andrew Wang (Cloudera)
• Benoy Antony (ebay)
• Jing Zhao (Hortonworks)
• Hiroshi Ideka (vic.co.jp)
• Eddy Xu (Cloudera)
• Steve Loughran (Hortonworks)
• Suresh Srinivas (Hortonworks)
• Kihwal Lee (Yahoo)
• Joep Rottinghuis (Twitter)
• Lohit VijayaRenu (Twitter)

@twitterhadoop
Questions and Answers
• For help setting up QoS, feature ideas, questions:
Ming Ma Chris Li
@twitterhadoop
@mingmasplace
chrili_sf@ebaysf.com

@twitterhadoop
FairCallQueue Data
• 37 node cluster
• 10 users runs a job which has:
• 20 Mappers, each mapper:
• Runs 100 threads. Each thread:
• Continuously calls hdfs.exists() in a tight loop
• Spikes are caused by garbage collection, a
separate issue

@twitterhadoop
Client Backoff Data
• See https://issues.apache.org/jira/secure/
attachment/12670619/
MoreRPCClientBackoffEvaluation.pdf

@twitterhadoop
Related JIRAs
• FairCallQueue + Backoff: HADOOP-9640
• Cross Data Center Trafﬁc QoS: HDFS-5175
• nntop: HDFS-6982
• Datanode Congestion Control: HDFS-7270
• Namenode ﬁne-grained locking: HDFS-5453

@twitterhadoop
Thoughts on Tuning
• Worth considering if you run a larger cluster or
have many users
• Make your life easier while tuning by refreshing the
queue with hadoop dfsadmin -refreshCallQueue

@twitterhadoop
Anatomy of a QoS conf key
• core-site.xml
• ipc.8020.faircallqueue.priority-levels
RPC server’s port, customize if using
non-default port / service rpc port

key: default:
@twitterhadoop
Number of Sub-queues
• More subqueues = more unique classes of service
• Recommend 10 for larger clusters
ipc.8020.faircallqueue.priority-levels 4

key: default:
@twitterhadoop
Scheduler: Decay Factor
• Controls by how much accumulated counts are
decayed by on each sweep. Larger values decay
slower.
• Ex: 1024 calls with decay factor of 0.5 will take 10
sweeps to decay assuming the user makes no
additional calls.
ipc.8020.faircallqueue.decay-scheduler.decay-factor 0.5

key: default:
@twitterhadoop
Scheduler: Sweep Period
• How many ms between each decay sweep. Smaller
is more responsive, but sweeps have overhead.
• Ex: if it takes 10 sweeps to decay and we sweep
every 5 seconds, a user’s activity will remain for
50s.
ipc.8020.faircallqueue.decay-scheduler.period-ms 5000

key: default:
@twitterhadoop
Scheduler: Thresholds
• List of ﬂoats, determines boundaries between each service class. If you
have 4 queues, you’ll have 3 bounds.
• Each number represents a percentage of total calls.
• First number is threshold for going into queue 0 (highest priority).
Second number decides queue 1 vs rest. etc.
• Recommend trying even splits (10, 20, 30, … 90) or exponential
(default)
ipc.8020.faircallqueue.decay-scheduler.thresholds 12%, 25%, 50%

key: default:
@twitterhadoop
Multiplexer: Weights
• Weights are how many times the mux will try to read from a sub-queue it
represents before moving on to the next sub-queue.
• Ex: 4,3,1 is used for 3 queues, meaning: Read up to 4 times from queue
0, Read up to 3 times from queue 1, Read once from queue 2, Repeat
• The mux controls the penalty of being in a low-priority queue.
Recommend not setting anything to 0, as starvation is possible in that
case.
ipc.8020.faircallqueue.multiplexer.weights 8,4,2,1

key: default:
@twitterhadoop
Backoff Max Attempts
• The default is equivalent to 90 seconds of retrying
• To achieve equivalent of 10 minutes of retrying, set
it to 44.
dfs.client.retry.max.attempts 10

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