Les vendredi noirs ? Même pas peur ! David Pilato Developer | Evangelist, @dadoonet

Data Platform Architectures

The Elastic Journey of Data Beats Log Files Wire Data Metrics your{beat} !3 @dadoonet sli.do/elastic

The Elastic Journey of Data Beats Log Files Elasticsearch Master Nodes (3) Wire Data Ingest Nodes (X) Metrics your{beat} Data Nodes Hot (X) Data Notes Warm (X) !4 @dadoonet sli.do/elastic

The Elastic Journey of Data Beats Log Files Elasticsearch Master Nodes (3) Wire Data Ingest Nodes (X) Metrics your{beat} Data Nodes Hot (X) Data Notes Warm (X) !5 @dadoonet sli.do/elastic Kibana Instances (X)

The Elastic Journey of Data Beats Elasticsearch Logstash Log Files Wire Data Master Nodes (3) Ingest Nodes (X) Metrics your{beat} Nodes (X) Data Nodes Hot (X) Data Notes Warm (X) !6 @dadoonet sli.do/elastic Kibana Instances (X)

The Elastic Journey of Data Beats Elasticsearch Logstash Log Files Wire Data Master Nodes (3) Ingest Nodes (X) Metrics your{beat} !7 Data Store Web APIs Social Sensors Nodes (X) Data Nodes Hot (X) Data Notes Warm (X) @dadoonet sli.do/elastic Kibana Instances (X)

The Elastic Journey of Data Beats Elasticsearch Logstash Log Files Wire Data Master Nodes (3) Kibana Ingest Nodes (X) Metrics your{beat} Data Store Web APIs Social Sensors Nodes (X) Data Nodes Hot (X) Data Notes Warm (X) Queues !8 Instances (X) @dadoonet Storage Metrics Notification sli.do/elastic

The Elastic Journey of Data Beats Elasticsearch Logstash Log Files Wire Data Master Nodes (3) Kibana Ingest Nodes (X) Metrics your{beat} Kafka Nodes (X) Redis Messaging Queue Data Store Web APIs Social Sensors Data Nodes Hot (X) Data Notes Warm (X) Queues !9 Instances (X) @dadoonet Storage Metrics Notification sli.do/elastic

App Search Site Search Enterprise Search Logging APM Business Analytics Security Analytics Metrics Elastic Stack Future Solutions Kibana Visualize & Manage Elasticsearch Store, Search, & Analyze Beats SaaS Elastic Cloud Logstash Ingest Self Managed Standalone Elastic Cloud Enterprise Deployment

Elasticsearch Cluster Sizing

Terminology Cluster my_cluster Server 1 Node A d1 d3 d6 d2 d4 d9 d12 d11 d7 d8 d5 d3 d1 d10 d4 Index twitter d6 d2 d5 Index logs !12 @dadoonet sli.do/elastic

Partition Cluster my_cluster Server 1 d1 4 d3 3 Node A d2 d4 d9 d12 2 Shards d6 d11 d7 d8 d5 d10 0 1 1 d3 d1 d4 Index twitter d6 d2 d5 0 Index logs !13 @dadoonet sli.do/elastic

Distribution Cluster my_cluster Server 1 Server 2 twitter shard P4 d1 d6 Node B Node A twitter shard P1 d2 d5 d3 d10 d11 twitter shard P0 d7 d8 d4 d9 twitter shard P3 d12 twitter shard P2 !14 d3 d1 @dadoonet d6 logs shard P0 d2 d4 d5 logs shard P1 sli.do/elastic

Replication • Primaries • Replicas Cluster my_cluster Server 1 Server 2 twitter shard P4 Node B d1 d6 d2 d11 d7 d3 twitter shard R3 d9 d12 twitter shard P2 !15 twitter shard P1 d5 d2 d2 d11 d5 d1 @dadoonet d6 d8 d9 logs shard R0 twitter shard P3 logs shard P0 twitter shard R2 twitter shard P0 d7 d6 d2 d4 d5 d10 d3 d1 twitter shard R1 d12 d3 Node A d4 twitter shard R0 logs shard R1 d4 d6 d3 d10 d8 d4 twitter shard R4 d1 d5 logs shard P1 sli.do/elastic

Replication Server 3 twitter shard R4 d1 d6 Server 2 Node C twitter shard P4 twitter shard P1 d2 d10 twitter shard R3 d9 twitter shard P2 !16 d6 Node A d2 d8 d4 d2 d3 d1 logs shard P1 @dadoonet logs shard R0 twitter shard P3 twitter shard R1 d12 d5 d5 d8 d9 d2 d6 logs shard P0 twitter shard R2 sli.do/elastic twitter shard P0 d7 d4 twitter shard R0 logs shard R1 d4 d11 d3 d7 d3 d4 Node B d1 d11 d5 d12 • Primaries • Replicas Cluster my_cluster Server 1 d5 d10 d1 d3 d6

Scaling Data !17 @dadoonet sli.do/elastic

Scaling Data !18 @dadoonet sli.do/elastic

Scaling Data !19 @dadoonet sli.do/elastic

Scaling Big Data … !20 @dadoonet … sli.do/elastic

Scaling Big Data … … • In Elasticsearch, shards are the working unit • More data -> More shards But how many shards? !21 @dadoonet sli.do/elastic

How much data? • ~1000 events per second • 60s * 60m * 24h * 1000 events => ~87M events per day • 1kb per event => ~82GB per day • 3 months => ~7TB !22 @dadoonet sli.do/elastic

Shard Size • It depends on many different factors ‒ document size, mapping, use case, kinds of queries being executed, desired response time, peak indexing rate, budget, … • After the shard sizing*, each shard should handle 45GB • Up to 10 shards per machine

• https://www.elastic.co/elasticon/conf/2016/sf/quantitative-cluster-sizing !23 @dadoonet sli.do/elastic

How many shards? • Data size: ~7TB • Shards per machine: 10* • Shard Size: ~45GB* • Total Servers: 16 • Total Shards: ~160 3 months of logs Cluster my_cluster … * https://www.elastic.co/elasticon/conf/2016/sf/quantitative-cluster-sizing !24 @dadoonet sli.do/elastic

But… • How many indices? • What do you do if the daily data grows? • What do you do if you want to delete old data? !25 @dadoonet sli.do/elastic

Time-Based Data • Logs, social media streams, time-based events • Timestamp + Data • Do not change • Typically search for recent events • Older documents become less important • Hard to predict the data size !26 @dadoonet sli.do/elastic

Time-Based Data • Time-based Indices is the best option ‒ create a new index each day, week, month, year, … ‒ search the indices you need in the same request !27 @dadoonet sli.do/elastic

Daily Indices Cluster my_cluster d3 d1 d4 d6 d2 d5 logs-2018-04-10 !28 @dadoonet sli.do/elastic

Daily Indices Cluster my_cluster d3 d1 d4 d6 d2 d5 logs-2018-04-10 d3 d1 d4 d6 d2 d5 logs-2018-04-11 !29 @dadoonet sli.do/elastic

Daily Indices Cluster my_cluster d3 d1 d4 d6 d2 d5 logs-2018-04-10 d3 d1 d4 d6 d2 d5 logs-2018-04-11 d3 d1 d4 d6 d2 d5 logs-2018-04-12 !30 @dadoonet sli.do/elastic

Templates • Every new created index starting with ‘logs-’ will have ‒ 2 shards ‒ 1 replica (for each primary shard) ‒ 60 seconds refresh interval PUT _template/logs { “template”: “logs-*”, “settings”: { “number_of_shards”: 2, “number_of_replicas”: 1, “refresh_interval”: “60s” } } More on that later !31 @dadoonet

Alias Cluster my_cluster d3 d1 d4 logs-write logs-2018-04-10 users @dadoonet d2 d5 logs-read Application !32 d6 sli.do/elastic

Alias Cluster my_cluster d3 d1 d4 logs-write d2 d5 logs-2018-04-10 d3 logs-read Application d6 d1 d4 d6 d2 d5 logs-2018-04-11 users !33 @dadoonet sli.do/elastic

Alias Cluster my_cluster d3 d1 d4 logs-write d2 d5 logs-2018-04-10 d3 logs-read Application d6 d1 d4 d6 d2 d5 logs-2018-04-11 d3 users d1 d4 d6 d2 d5 logs-2018-04-12 !34 @dadoonet sli.do/elastic

Detour: Rollover API https://www.elastic.co/guide/en/elasticsearch/reference/6.3/indices-rollover-index.html

Do not Overshard don’t keep default values! Cluster my_cluster d3 d6 d1 • 3 different logs d2 d4 d5 • 1 index per day each access-… • 1GB each d5 • 5 shards (default): so 200mb / shard vs 45gb d1 d7 d6 d9 d5 • 6 months retention application-… • ~900 shards for ~180GB d59 • we needed ~4 shards! d0 d4 d10 d3 d5 mysql-… !36 @dadoonet sli.do/elastic

Scaling Big Data 1M users … … But what happens if we have 2M users? !37 @dadoonet sli.do/elastic

Scaling Big Data !38 1M users … … 1M users … … @dadoonet sli.do/elastic

Scaling Big Data !39 1M users … … 1M users … … 1M users … … @dadoonet sli.do/elastic

Scaling Big Data U s e r s !40 … … … … … … @dadoonet sli.do/elastic

Shards are the working unit • Primaries ‒ More data -> More shards ‒ write throughput (More writes -> More primary shards) • Replicas ‒ high availability (1 replica is the default) ‒ read throughput (More reads -> More replicas) !41 @dadoonet sli.do/elastic

Detour: Shrink API https://www.elastic.co/guide/en/elasticsearch/reference/6.3/indices-shrink-index.html

Detour: Split API https://www.elastic.co/guide/en/elasticsearch/reference/6.3/indices-split-index.html

Optimal Bulk Size

What is Bulk? 1000 index requests with 1 document __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ _____ Master Nodes (3) Ingest Nodes (X) Beats Logstash Application Data Nodes Hot (X) 1000 log events 1 bulk request with 1000 documents !45 Elasticsearch @dadoonet sli.do/elastic Data Notes Warm (X)

What is the optimal bulk size? Elasticsearch

Beats Logstash Application 1000 log events !46 @dadoonet 4 * 250? 2 * 500? 1 * 1000? Master Nodes (3) Ingest Nodes (X) Data Nodes Hot (X) Data Notes Warm (X) sli.do/elastic

It depends… • on your application (language, libraries, …) • document size (100b, 1kb, 100kb, 1mb, …) • number of nodes • node size • number of shards • shards distribution !47 @dadoonet sli.do/elastic

Test it ;) Elasticsearch

1000000 log events 4000 * Beats Logstash Application 2000 * 250-> 160s Master Nodes (3) 500-> 164s Ingest Nodes (X) 1000 * 1000-> 155s Data Nodes Hot (X) Data Notes Warm (X) !48 @dadoonet sli.do/elastic

Test it ;) input { stdin{} } filter {} output { elasticsearch { hosts => [“10.12.145.189”] flush_size => “${SIZE}” } } In Beats set “bulk_max_size” in the output.elasticsearch DATE=date +%Y.%m.%d LOG=logs/logs.txt exec_test () { curl -s -XDELETE “http://USER:PASS@HOST:9200/logstash-$DATE” sleep 10 export SIZE=$1 time cat $LOG | ./bin/logstash -f logstash.conf } for SIZE in 100 500 1000 3000 5000 10000; do for i in {1..20}; do exec_test $SIZE done; done; !49 @dadoonet sli.do/elastic

Test it ;) • 2 node cluster (m3.large) ‒ 2 vCPU, 7.5GB Memory, 1x32GB SSD • 1 index server (m3.large) ‒ logstash ‒ kibana !50

docs

100 500 1000 3000 5000 10000 time(s) 191.7 161.9 163.5 160.7 160.7 161.5 @dadoonet

Distribute the Load

Avoid Bottlenecks Elasticsearch single node _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ _________ Node 1 Beats Logstash Application 1000000 log events round robin Node 2 output { elasticsearch { hosts => [“node1”,”node2”] } } !52 @dadoonet sli.do/elastic

Load Balancer Elasticsearch

Node 1 Beats Logstash LB Application Node 2 1000000 log events !53 @dadoonet sli.do/elastic

Coordinating-only Node Elasticsearch

Node 1 Beats Logstash Node 3 co-node Application Node 2 1000000 log events !54 @dadoonet sli.do/elastic

Test it ;) • 2 node cluster (m3.large) ‒ 2 vCPU, 7.5GB Memory, 1x32GB SSD • 1 index server (m3.large) ‒ logstash (round robin configured) ‒ hosts => [“10.12.145.189”, “10.121.140.167”] ‒ kibana !55 #docs time(s) 1000 5000 10000 NO Round Robin 163.5 160.7 161.5 Round Robin 161.3 158.2 159.4 @dadoonet sli.do/elastic

Optimizing Disk IO

Durability time buffer index a doc buffer index a doc buffer index a doc buffer !57 @dadoonet lucene flush segment

refresh_interval • Dynamic per-index setting PUT logstash-2017.05.16/_settings { “refresh_interval”: “60s” } • Increase to get better write throughput to an index • New documents will take more time to be available for Search. !58 #docs time(s) 1000 5000 10000 1s refresh 60s refresh 161.3 156.7 158.2 152.1 159.4 152.6 @dadoonet

Durability time buffer index a doc doc op trans_log buffer lucene flush segment trans_log buffer elasticsearch flush lucene commit trans_log !59 @dadoonet segment segment

Translog fsync every 5s (1.7) buffer Primary index a doc doc op trans_log buffer Replica index a doc doc op trans_log redundancy doesn’t help if all nodes lose power !60 @dadoonet

Translog fsync on every request • For low volume indexing, fsync matters less • For high volume indexing, we can amortize the costs and fsync on every bulk • Concurrent requests can share an fsync bulk 1 single fsync bulk 2 !61 @dadoonet sli.do/elastic

Async Transaction Log • index.translog.durability ‒ request (default) ‒ async • index.translog.sync_interval (only if async is set) • Dynamic per-index settings • Be careful, you are relaxing the safety guarantees !62 #docs time(s) 1000 5000 10000 Request fsync 161.3 158.2 159.4 5s sync 152.4 149.1 150.3 @dadoonet

Final Remarks

App Search Site Search Enterprise Search Logging APM Business Analytics Security Analytics Metrics Elastic Stack Future Solutions Kibana Visualize & Manage Elasticsearch Store, Search, & Analyze Beats SaaS Elastic Cloud Logstash Ingest Self Managed Standalone Elastic Cloud Enterprise Deployment

Final Remarks • Primaries ‒ More data -> More shards ‒ Do not overshard! • Replicas ‒ high availability (1 replica is the default) ‒ read throughput (More reads -> More replicas) Big Data U s e r s !65 @dadoonet … … … … … … sli.do/elastic

Final Remarks • Bulk and Test • Distribute the Load • Refresh Interval • Async Trans Log (careful) !66 #docs per bulk 1000 5000 10000 Default 163.5 160.7 161.5 RR+60s+Async5s 152.4 149.1 150.3 @dadoonet

Les vendredi noirs ? Même pas peur ! David Pilato Developer | Evangelist, @dadoonet