HBase Operations and Best Practices

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BigData Architecture, HBase Setup, HBase Operations and Best Practices

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  • Venu, Thanks for sharing these slides . Can you pls elaborate why on Slide 39 under operational best practices you have mentioned 'No SSDs, No virtualized storage'. Is this in general for Hadoop deployments or is this for region servers /slaves? Pls elucidate. Thanks.
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  • C:Consistency – When you write a tuple, its immediately available for readA: Availability – Loosing a node, will not bring the cluster downP: Partition Tolerance – Data is sharded across nodes, so if you loose group of nodes, its still available Cassandra - AP
  • HBase Operations and Best Practices

    1. 1. HBase Operations & Best Practices Venu Anuganti July 2013 http://scalein.com/ Blog: http://venublog.com/ Twitter: @vanuganti
    2. 2. Who am I o Data Architect, Technology Advisor o Founder of ScaleIN, Data Consulting Company, 5+ years o 100+ companies, 20+ from Fortune 200 o http://scalein.com/ o Architect, Implement & Support SQL, NoSQL and BigData Solutions  Industry: Databases, Games, Social, Video, SaaS, Analytics, Warehouse, Web, Financial, Mobile, Advertising & SEM Marketing
    3. 3. Agenda  BigData - Hadoop & HBase Overview  BigData Architecture  HBase Cluster Setup Walkthrough  High Availability  Backup and Restore  Operational Best Practices
    4. 4. BigData Overview
    5. 5. BigData Trends • BigData is the latest industry buzz, many companies adopting or migrating o Not a replacement for OLTP or RDBMS systems • Gartner – 28B in 2012 & 34B in 2013 spend o 2013 top-10 technology trends – 6th place • Solves large data problems that existed for years o Social, User, Mobile growth demanded such a solution o Google “BigTable” is the key, followed by Amazon “Dynamo”; new papers like Dremel drives it further o Hadoop & ecosystem is becoming synonym for BigData • Combines vast structured/un-structured data o Overcomes from legacy warehouse model o Brings data analytics & data science o Real-time, mining, insights, discovery & complex reporting
    6. 6. BigData • Key factors - Pros  Can handle any size  Commodity hardware  Scalable, Distributed, Highly Available  Ecosystem & growing community • Key factors – Cons  Latency  Hardware evolution, even though designed for commodity  Does not fit for all
    7. 7. BigData Architecture
    8. 8. Low Level Architecture
    9. 9. Why HBase
    10. 10. Why HBase • HBase is proven, widely adopted  Tightly coupled with hadoop ecosystem  Almost all major data driven companies using it • Scales linearly  Read performance is its core; random, sequential reads  Can store tera/peta bytes of data  Large scale scans, millions of records  Highly distributed • CAP Theorem – HBase is CP driven • Competition: Cassandra (AP)
    11. 11. Hadoop/HBase Cluster Setup
    12. 12. Cluster Components 3 Major Components  Master(s)  HMaster  Coordination  Zookeeper  Slave(s)  Region server Name Node HMaster Zookeeper MASTER Data Node Region Server SLAVE 1 Data Node Region Server SLAVE 3 Data Node Region Server SLAVE 2
    13. 13. How It Works HMASTERDDLCLIENT HDFS REGION SERVERS RS RS RS ZOOKEEPER CLUSTER ZK ZK ZK
    14. 14. Zookeeper  Zookeeper o Coordination for entire cluster o Master selection o Root region server lookup o Node registration o Client always communicates with Zookeper for lookups (cached for sub-sequent calls) hbase(main):001:0> zk "ls /hbase" [safe-mode, root-region-server, rs, master, shutdown, replication]
    15. 15. Zookeeper Setup  Zookeeper • Dedicated nodes in the cluster • Always in odd number • Disk, memory, cpu usage is low • Availability is a key
    16. 16. Master Node  HMaster o Typically runs with Name Node o Monitors all region servers, handles RS failover o Handles all meta data changes o Assigns regions o Interface for all meta data changes o Load balancing on idle times
    17. 17. Master Setup • Dedicated Master Node o Light on use, but should be on reliable hardware o Good amount of memory and CPU can help o Disk space is pretty nominal • Must Have Redundancy o Avoid single point of failure (SPOF) o RAID preferred for redundancy or even JBOD o DRBD or NFS is also preferred
    18. 18. Region Server  Region Server o Handles all I/O requests o Flush MemStore to HDFS o Splitting o Compaction o Basic element of table storage o Table => Regions => Store per Column Family => CF => MemStore / CF/Region && StoreFile /Store/Region => Block o Maintains WAL (Write Ahead Log) for all changes
    19. 19. Region Server - Setup • Should be stand-alone and dedicated o JBOD disks o In-expensive o Data node and region server should be co-located • Network o Dual 1G, 10G or InfiniBand, DNS lookup free • Replication - at least 3, locality • Region size for splits; too many or too small regions are not good.
    20. 20. Cluster Setup – 10 Node
    21. 21. High Availability
    22. 22. High Availability • HBase Cluster - Failure Candidates  Data Center  Cluster  Rack  Network Switch  Power Strip  Region or Data Node  Zookeeper Node  HBase Master  Name Node
    23. 23. HA - Data Center • Cross data center, geo distributed • Replication is the only solution  Up2date data  Active-active  Active-passive  Costly (can be sized)  Need dedicated network • On-demand offline cluster  Only for disaster recovery  No up2date copy  Can be sized appropriately  Need to reprocess for latest data
    24. 24. HA – Redundant Cluster • Redundant cluster within a data center using replication • Mainly to have backup cluster for disasters  Up2date data  Restore a state back using TTL based  Restore deleted data by keeping deleted cells  Run backups  Read/write distributed with load balancer  Support development or provide on-demand data  Support low important activities • Best practice: Avoid redundant cluster, rather have one big cluster with high redundancy
    25. 25. HA – Rack, Network, Power • Cluster nodes should be rack and switch aware • Loosing a rack or a network switch should not bring cluster down • Hadoop has built-in rack awareness  Assign nodes based on rack diagram  Redundant nodes are within rack, across switch and rack  Manual or automatic setup to detect location • Redundant power and network within each node (master)
    26. 26. HA – Region Servers • Loosing a region server or data node is very common, in many cases it could be very frequent • They are distributed and replicated • Can be added/removed dynamically, taken out for regular maintenance • Replication factor of 3 – Can loose ⅔rd of the cluster nodes • Replication factor of 4 – Can loose ¾th of the cluster nodes
    27. 27. HA – Zookeeper • Zookeeper nodes are distributed • Can be added/removed dynamically • Should be implemented in odd number, due to quorum (majority voting wins the active state) • If 4, can loose 1 node (3 major voting) • If 5, can loose 2 nodes (3 major voting) • If 6, can loose 2 nodes (4 major voting) • If 7, can loose 3 nodes (4 major voting) • Best Practice: 5 or 7 with dedicated hardware.
    28. 28. HA – HMaster • HMaster - single point of failure • HA - Multiple HMaster nodes within a cluster  Zookeeper co-ordinates master failure  Only one active at any given point of time  Best practice: 2-3 HMasters, 1 per rack
    29. 29. Scalability
    30. 30. How to scale • By design, cluster is highly distributed and scalable • Keep adding more region servers to scale  Region splits  Replication factor  Row key design is a key factor for scaling writes  No single “hot” region  Bulk loading, pre-split  Native java access X other protocols like thrift  Compaction at regular intervals
    31. 31. Performance  Benchmarking is a key • Nothing fits for all • Simulate use cases and run the tests oBulk loading oRandom access, read/write oBulk processing oScan, filter • Negative performance oReplication factor oZookeeper nodes oNetwork latency oSlower disks, CPUs oHot regions, Bad row key or Bulk loading without pre-splits
    32. 32. Tuning  Tune the cluster to best fit the environment • Block Size, LRU cache, 64K default, per CF • JBOD • Memstore • Compaction, manual • WAL flush • Avoid long GC pauses, JVM • Region size, small is better, split based on “hot” • Batch size • In-memory column families • Compression, LZO • Timeouts • Region handler count, threads/region • Speculative execution • Balancer, manual
    33. 33. Backup & (Point-in-time ) Restore
    34. 34. Backup - Built-in • In general no external backup needed • HBase is highly distributed and has built-in versioning, data retention policy  No need to backup just for redundancy  Point-in-time restore: • Use TTL/Table/CF/C and keep the history for X hours/days  Accidental deletes: • Use ‘KeepDeletedCells’ to keep all deleted data
    35. 35. Backup - Tools • Use Export/Import tool  Based on timestamp; and use it for point-in-time backup/restore • Use region snapshots  Take HFile snapshots and copy them over to new storage location  Copy Hlog files for point-in-time roll-forward from snapshot time (replay using WALPlayer post import).  Table snapshots (0.94.6+)
    36. 36. Backup - Replication • Use replicated cluster as one of the backup / disaster recovery • Statement based, write ahead log (WAL, HLog) from each region server  Asynchronous  Active Active using 1-1 replication  Active Passive using 1-N replication  Can be of same or different node size  0.92 onwards Active Active possible
    37. 37. Operational Best Practices
    38. 38. Hardware • Commodity Hardware • 1U or 2U preferred, avoid 4U or NAS or expensive systems • JBOD on slaves, RAID 1+0 on masters • No SSDs, No virtualized storage • Good number of cores (4-16), HT enabled • Good amount of RAM (24-72G) • Dual 1G network, 10G or InfiniBand
    39. 39. Disks • SATA, 7/10/15K, cheaper the better • Use RAID firmware drives, faster error detection & enable disks to fail on h/w errors • Limit to 6/8 drives on 8 core, allow 1 drive/core = 100 IOPS/Drive = 4 * 1T = 4T, 400 IOPS, 400MB = 8 * 500G = 4T, 800 IOPS = not beyond 800/900MB/sec due to n/w saturation • Ext3/ext4/XFS • Mount => noatime, nodiratime
    40. 40. OS, Kernel • RHEL or CentOS or Ubuntu • Swappiness=0, and no swap files • File limits to hadoop user (/etc/security/limits.conf) => 64/128K • JVM GC, HBase heap • NTP • Block size
    41. 41. Automation • Automation is a key in distributed cluster setup  To easily launch a new node  To restore to base state  Keep same packages, configurations across the cluster • Use puppet/Chef/Existing process  Keep as much as possible puppetized  No accidental upgrades as it can restart the service • Cloudera Manager (CM) for any node management tasks  You can also puppetize & automate the process  CM will install all necessary packages
    42. 42. Load Balancer • Internal  Periodically run balancer to ensure data distribution among region servers • hadoop-daemon.sh start balancer -threshold 10 • External  Has built-in load balancing capability  If using thrift bindings; then thrift servers needs to be load balanced  Future versions will address thrift balancing as well
    43. 43. Upgrades • In general upgrades should be well planned • To update changes to cluster nodes (OS, configs, hardware, etc.); you can also do rolling restart without taking cluster down • Hadoop/HBase supports simple upgrade paths with rollback strategy to go back to old version • Make sure HBase/Hadoop versions are compatible • Use rolling restart for minor version upgrades
    44. 44. Monitoring • Quick Checks  Use built-in web tools  Cloudera manager  Command line tools or wrapper scripts • RRD, Monitoring  Cloudera manager  Ganglia, Cacti, Nagios, NewRelic  OpenTSDB  Need proper alerting system for all events  Threshold monitoring for any surprises
    45. 45. Alerting System  Need proper alerting system  JMX exposes all metrics  Ops Dashboard (Ganglia, Cacti, OpenTSDB, NewRelic)  Small dashboard for critical events  Define proper levels for escalation  Critical  Loosing a Master or ZooKeeper Node  +/- 10% drop in performance or latency  Key thresholds (load, swap, IO)  Loosing 2 or more slave nodes  Disk failures  Loosing a single slave node (critical in prime time)  Un-balanced nodes  FATAL errors in logs
    46. 46. Case Study
    47. 47. Case Study - 1 • 110 node cluster  Dual Quad Core, Intel Xeon, 2.2GHz  48G, no swap  6 2T SATA, 7K  Ubuntu 11.04  Puppet  Fabric for running commands on all nodes  /home/hadoop is everything, symlinks  Nagios  OpenTSDB for Trending points, dashboard  M/R limited to 50% of available RAM
    48. 48. Questions ? • http://scalein.com/ • http://venublog.com/ • venu@venublog.com • Twitter: @vanuganti

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