Scaling with sync_replication using Galera and EC2


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Challenging architecture design, and proof of concept on a real case of study using Syncrhomous solution.
Customer asks me to investigate and design MySQL architecture to support his application serving shops around the globe.
Scale out and scale in base to sales seasons.

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Scaling with sync_replication using Galera and EC2

  1. 1. Scaling MySQL using multi master synchronous replication Marco “the Grinch” Tusa Percona Live London2013
  2. 2. About Me Introduction Marco “The Grinch” • Former Pythian cluster technical leader • Former MySQL AB PS (EMEA) • Love programming • History of religions • Ski; Snowboard; scuba diving; Mountain trekking
  3. 3. Agenda • Customer requirements • Installation and initial setup • Applying the customer scenario to solution • Numbers, and considerations. • Scaling out test and efforts • Scaling in test and efforts • Geographic distribution Introduction
  4. 4. Many Galera Talks • PERCONA XTRADB CLUSTER IN A NUTSHELL : HANDS ON TUTORIAL Tutorial Monday • Galera Cluster 3.0 New Features. Seppo Jaakola Presentation Tuesday • HOW TO UNDERSTAND GALERA REPLICATION Alexey Yurchenko Presentation Tuesday Introduction
  5. 5. A journey started 2 yeas ago • First work done as POC in November 2011 • First implementation in production January 2012 • Many more after • Last done 12 clusters of 5 nodes with 18 to 24 application server attached Introduction
  6. 6. Historical Real life case Customer mentions the need to scale for writes. My first though went to NDB. Customer had specific constrains: • Amazon EC2; • No huge instances (medium preferred); • Number of instances Increase during peak seasons; • Number of Instances must be reduced during regular period; • Customer use InnoDB as storage engine in his current platform and will not change; Customer requirements
  7. 7. Refine the customer requirements Challenging architecture design, and proof of concept on a real case of study using Synchronous solution. Customer asks us to investigate and design MySQL architecture to support his application serving shops around the globe. Scale out and scale in base to sales seasons. We will share our experience presenting the results of our POC High level outline Customer numbers: • Range of operation/s from 20 to 30,000 (5.000 inserts/sec) • Selects/Inserts 70/30 % • Application servers from 2 to ∞ • MySQL servers from 2 to ∞ • Operation from 20 bytes to max 1Mb (text) • Data set dimension 40GB (Old data is archive every year) • Geographic distribution (3 -> 5 zones), partial dataset Customer requirements
  8. 8. My Motto Use the right tool for the job Customer requirements
  9. 9. Scaling Up vs. Out Scaling Up Model • Require more investment • Not flexible and not a good fit with MySQL Scaling Out Model • Scale by small investment • Flexible • Fits in MySQL model (HA, load balancing etc.)
  10. 10. Scaling Reads vs Write • Read Easy way of doing in MySQL if % of write is low Write Read •Write • Replication is not working • Single process • No data consistency check • Parallel replication by schema is not • Semi synchronous replication is not the solution the solution as well
  11. 11. Synchronous Replication in MySQL MySQL cluster, I NDBCluster • Really synchronous • Data distribution and Internal partitioning • The only real solution giving you 9 9. 9 9 9 % (5 minutes) max downtime • NDB Cluster is more then a simple storage engine (use API if you can) Galera replication • Virtually Synchronous • No data consistency check (optimistic lock) • Data replicated by Commit • Use InnoDB Options Overview
  12. 12. Choosing the solution Did I say I NDB Cluster? –But not a good fit here because: •EC2 dimension (1 CPU 3.7GB RAM); •Customer does not want to change from InnoDB; •Need to train the developer to get out maximum from it; –Galera could be a better fit because: •Can fit in the EC2 dimension; •Use InnoDB; •No additional knowledge when develop the solution; Options Overview
  13. 13. Architecture Design Final architecture simple and powerful
  14. 14. Architecture Design Application layer in the cloud Load Balancer distributing request in RR Data layer in the cloud MySQL instance geographically distributed Architecture AWS blocks EC2 small instance EC2 medium instance
  15. 15. Instances EC2 Web servers • Small instance • Local EBS Data Servers • Medium instance 1 CPU 3.7GB RAM • 1 EBS OS • 6 EBS RAID0 for data Be ready to scale OUT • Create an AMI • Get AMI update at regular base Architecture EC2 blocks
  16. 16. Why not ephemeral storage RAID0 against 6 EBS is performing faster; • RAID approach will mitigate possible temporary degradation; • Ephemeral is … ephemeral, all data will get lost; Numbers with rude comparison (ebs) Timing buffered disk reads: 768 MB in 3.09 seconds = 248.15 MB/sec (eph)Timing buffered disk reads: 296 MB in 3.01 seconds = (ebs)Timing O_DIRECT disk reads: 814 MB in 3.20 seconds = 254.29 MB/sec (eph)Timing O_DIRECT disk reads: 2072 MB in Architecture Installation and numbers 98.38 MB/sec 3.00 seconds = 689.71 MB/sec
  17. 17. Why not ephemeral storage (cont.) Architecture Installation and numbers
  18. 18. Why not ephemeral storage (cont.) Architecture Installation and numbers
  19. 19. Why not ephemeral storage (cont.) Architecture Installation and numbers
  20. 20. Storage on EC2 Multiple EBS RAID0 Or USE Provisioned IOPS Amazon EBS Provisioned IOPS volumes: Amazon EBS Standard volumes: $0.10 per GB-month of provisioned storage $0.125 per GB-month of provisioned storage $0.10 per provisioned IOPS-month $0.10 per 1 million I/O requests Architecture Installation and numbers
  21. 21. Instances EC2 How we configure the EBS. • Use Amazon EC2 API Tools ( • Create 6 EBS • Attach them to the running instance Run mdadm as root (sudo mdadm --verbose --create /dev/md0 --level=0 --chunk=256 -- raid-devices=6 /dev/xvdg1 /dev/xvdg2 /dev/xvdg3 /dev/xvdg4 /dev/xvdg5 /dev/xvdg6 echo 'DEVICE /dev/xvdg1 /dev/xvdg2 /dev/xvdg3 /dev/xvdg4 /dev/xvdg5 /dev/xvdg6' | tee -a /etc/mdadm.conf sudo ) Create an LVM to allow possible easy increase of data size Format using ext3 (no journaling) Mount it using noatime nodiratime Run hdparm –t [--direct] <device> to check it works properly mdadm --detail --scan | sudo tee -a /etc/mdadm.conf Installation
  22. 22. Instances EC2 (cont.) You can install MySQL using RPM, or if you want to have a better life and upgrade (or downgrade) faster do: •Create a directory like /opt/mysql_templates •Get MySQL binary installation and expand it in the /opt/mysql_templates •Create symbolic link /usr/local/mysql against the version you want to use •Create the symbolic links also in the /usr/bin directory ie (for bin in `ls -D /usr/local/mysql/bin/`; do ln -s /usr/local/mysql/bin/$bin /usr/bin/$bin; done) Installation
  23. 23. Create the AMI • Once I had the machines ready and standardized. o o • Create AMI for the MySQL –Galera data node; Create AMI for Application node; AMI will be used for expanding the cluster and or in case of crashes. Installation
  24. 24. Problem in tuning - MySQL MySQL optimal configuration for the environment • • Dirty page; • Innodb write/read threads; • Binary logs (no binary logs unless you really need them); • Doublebuffer; • Setup Correct Buffer pool, InnoDB log size; Innodb Flush log TRX commit & concurrency;
  25. 25. Problem in tuning - Galera Galera optimal configuration for the environment evs.send_window Maximum messages in replication at a time • evs.user_send_window Maximum data messages in replication at a time • wsrep_slave_threads which is the number of threads used by Galera to commit the local queue • gcache.size • Flow Control • Network/keep alive settings and WAN replication • Setup
  26. 26. Applying the customer scenario How I did the tests. What I have used. Stresstool (my development) Java • • • • • • • • • Test application Multi thread approach (each thread a connection); Configurable number of master table; Configurable number of child table; Variable (random) number of table in join; Can set the ratio between R/W/D threads; Tables can have Any data type combination; Inserts can be done simple or batch; Reads can be done by RANGE, IN, Equal; Operation by set of commands not single SQL;
  27. 27. Applying the customer scenario (cont.) How I did the tests. • Application side • I have gradually increase the number of thread per instance of stresstool running, then increase the number of instance. • Data layer • Start with 3 MySQL; • Up to 7 Node; • Level of request • From 2 Application blocks to 12; • From 4 threads for “Application” block; • To 64 threads for “Application” block (768); Test application
  28. 28. Numbers Table with numbers (writes) for 3 nodes cluster and bad replication traffic Bad commit behavior
  29. 29. Numbers in Galera replication What happened to the replication? Bad commit behavior
  30. 30. Changes in replication settings Problem was in commit efficiency & Flow Control Reviewing Galera documentation I choose to change: • evs.send_window=1024 (Maximum packets in replication at a time.); • evs.user_send_window=1024 (Maximum data packets in replication at a time); • wsrep_slave_threads=48; Bad commit behavior
  31. 31. Numbers After changes (cont.) Table with numbers (writes) for 3-5-7 nodes and increase traffic Using MySQL 5.5
  32. 32. Numbers After changes (cont.) Table with numbers (writes) for 3-5-7 nodes and increase traffic Using MySQL 5.5
  33. 33. Other problems… This is what happen if one node starts to have issue? Tests & numbers
  34. 34. Numbers After changes (cont.) Rebuild the node, re-attach it to the cluster and the status is: Tests & numbers
  35. 35. Numbers After changes (cont.) Going further and removing Binary log writes: Tests & numbers
  36. 36. Numbers for reads traffic Select for 3 -7 nodes cluster and increase Tests & numbers
  37. 37. Many related metrics From 4 – 92 threads Tests & numbers Real HW
  38. 38. FC on real HW From 4 – 92 threads Tests & numbers Real HW
  39. 39. How to scale OUT The effort to scale out is: • Launch a new instance from AMI (IST Sync if wsrep_local_cache_size big enough otherwise SST); • Always add nodes in order to have ODD number of nodes; Modify the my.cnf to match the server ID and IP of the master node; • • Start MySQL • Include node IP in the list of active IP of the load balancer • The whole operation normally takes less then 30 minutes. Scaling
  40. 40. How to scale IN The effort to scale IN is minimal: Remove the data nodes IP from load balancer (HAProxy); • Stop MySQL • Stop/terminate the instance • Scaling
  41. 41. How to Backup: If using provisioning and one single volumes contains al, snapshot is fine. Otherwise I like the Jay solution: /taking-backups-percona-xtradb-cluster-withoutstalls-flow-control/ Using wsrep_desync=OFF
  42. 42. Failover and HA With MySQL and Galera, unless issue all the nodes should contain the same data. Performing failover will be not necessary for the whole service. Cluster in good health Cluster with failed node So the fail over is mainly an operation at load balancer (HAProxy works great) and add another new Instance (from AMI).
  43. 43. Geographic distribution With Galera it is possible to set the cluster to replicate cross Amazon’s zones. I have tested the implementation of 3 geographic location: • Master location (1 to 7 nodes); • First distributed location (1 node to 3 on failover); • Second distributed location (1 node to 3 on failover); No significant delay were reported, when the distributed nodes remain passive. • Good to play with: keepalive_period inactive_check_period suspect_timeout inactive_timeout install_timeout Geographic distribution
  44. 44. Problems with Galera During the tests we face the following issues: • MySQL data node crash auto restart, recovery (Galera in loop) • Node behind the cluster, replica is not fully synchronous, so the local queue become too long, slowing down the whole cluster • Node after restart acting slowly, no apparent issue, no way to have it behaving as it was before clean shutdown, happening randomly, also possible issue due to Amazon. Faster solution build another node and attach it in place of the failing. Conclusions
  45. 45. Did we match the expectations? Our numbers were: • From 1,200 to ~10,000 (~3,000 in prod) inserts/sec • 27,000 reads/sec with 7 nodes • From 2 to 12 Application servers (with 768 request/sec) • EC2 medium 1 CPU and 3.7GB!! o In Prod Large 7.5GB 2 CPU. I would say mission accomplished! Conclusions
  46. 46. Consideration about the solution Pro • Flexible; • Use well known storage engine; • Once tuned is “stable” (if Cloud permit it); Cons • !WAS! New technology not included in a official cycle of development; • Some times fails without clear indication of why, but is getting better; • Replication is still not fully Synchronous (on write/commit); Conclusions
  47. 47. Monitoring Control what is going on is very important Few tool are currently available and make sense for me: Jay Janssen ClusterControl for MySQL Percona Cacti monitor template Conclusions
  48. 48. Q&A
  49. 49. Thank you To contact Me To follow me @marcotusa Conclusions