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Improving HDFS Availability with Hadoop RPC Quality of Service

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Ming Ma

Heavy users monopolizing cluster resources is a frequent cause of slowdown for others. With only one namenode and thousands of datanodes, any poorly written application is a potential distributed denial-of-service attack on namenode. In this talk, you will learn how to prevent slowdown from heavy users and poorly-written applications by enabling IPC Quality of Service (QoS), a new feature in Hadoop 2.6+. On Twitter’s and eBay’s production clusters, we’ve seen response times of 500 milliseconds with QoS off drop to 10 milliseconds with QoS on during heavy usage. We’ll cover how IPC QoS works and share our experience on how to tune performance.

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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 • hadoop fs -ls takes 5+ seconds • Worst case: cluster outage • Namenode lost some datanode heartbeats → replication storm
@twitterhadoop Audit Logs to the Rescue • 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"); } 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 Bad Code + MapReduce = DDoS on Namenode! Namenode Bad User Good Users Other Users
@twitterhadoop Bad Code + MapReduce = DDoS on Namenode! Namenode Bad User Good Users Other Users
@twitterhadoop Bad Code + MapReduce = DDoS on Namenode! Namenode Bad User Good Users Other Users
@twitterhadoop Bad Code + MapReduce = DDoS on Namenode! Namenode Bad User Good Users Other Users
@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 Good User Bad User FIFO Call Queue HandlersReaders
@twitterhadoop Diagnosing Congestion HandlersReaders Good User Bad User
@twitterhadoop Diagnosing Congestion HandlersReaders Good User Bad User ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••
@twitterhadoop Solutions we’ve considered • 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 11%
@twitterhadoop Fair Scheduling: Good User Call Queue HandlersReaders Good User Bad User Queue 0: < 12%
@twitterhadoop Fair Scheduling: Good User Call Queue HandlersReaders Good User Bad User
@twitterhadoop Fair Scheduling: Bad User Call Queue HandlersReaders Good User Bad User
@twitterhadoop Fair Scheduling: Bad User Call Queue HandlersReaders Good User Bad User 80%
@twitterhadoop Fair Scheduling: Bad User Call Queue HandlersReaders Good User Bad User Queue 3: > 50%
@twitterhadoop Fair Scheduling: Bad User Call Queue HandlersReaders Good User Bad User
@twitterhadoop Fair Scheduling Result Call Queue HandlersReaders Good User Bad User
@twitterhadoop Weighted Round-Robin Multiplexing Call Queue HandlersReaders Good User Bad User
@twitterhadoop Weighted Round-Robin Multiplexing Call Queue HandlersReaders Good User Bad User Take 3
@twitterhadoop Weighted Round-Robin Multiplexing Call Queue HandlersReaders Good User Bad User Take 2
@twitterhadoop Weighted Round-Robin Multiplexing Call Queue HandlersReaders Good User Bad User
@twitterhadoop Weighted Round-Robin Multiplexing Call Queue HandlersReaders Good User Bad User
@twitterhadoop Weighted Round-Robin Multiplexing 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 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 Appendix
@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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Improving HDFS Availability with Hadoop RPC Quality of Service