Using Elasticsearch for Analytics

<ul><li> Using Elasticsearch for Analytics How we use Elasticsearch for Analytics at Wingify? Vaidik Kapoor github.com/vaidik twitter.com/vaidikkapoor </li> <li> Problem Statement VWO collects number of visitors and conversions per goal per variation for every campaign created. These numbers are used by our customers to make optimization decisions - very useful but limiting as these numbers are overall numbers and drilling down was not possible. There is a need to develop an analytics engine: capable of storing millions of daily data points, essentially JSON docs. should expose flexible and powerful query interface for segmenting visitors and conversions data. This is extremely useful for our customers to derive insights. querying should not be extremely slow - response times of 2-5 seconds are acceptable. not too difficult to maintain in production - operations should be easy for a lean team. should be easy to extend to provide new features. </li> <li> A distributed near real-time search engine, also considered as an analytics engine since a lot of people use it that way - proven solution. Highly available, fault tolerant, distributed - built from the ground up to work in the cloud. Elasticsearch is distributed - cluster management takes care of node downtimes which makes operations rather easy instead of being a headache. Application development remains the same no matter how you deploy Elasticsearch i.e. a cluster or single node. Capable of performing all the major types of searches, matches and aggregations. Also supports limited Regular Expressions. Easy index and replica creation on live cluster. Easy management of cluster and indices through REST API. to the rescue </li> <li> 1. Store a document for every unique visitor per campaign in Elasticsearch. Document contains: a. Visitor related segment properties like geo data, platform information, referral, etc. b. Information related to conversion of goals 2. Use Nested Types for creating hierarchy between every unique visitors visit and conversions. 3. Use Aggregations/Facets framework for generating datewise count of visitors and conversions and basic stats like average and total revenue, sum of squares of revenue, etc. 4. Never use script facets/aggs to get counts of a combination of values from the same document. Scripts are slow. Instead index result of script at index time. Visitor documents in Elasticsearch: { "account": 196, "experiment": 77, "combination": "5", "hit_time": "2014-07-09T23:21:15", "ip": "71.12.234.0" "os": "Android", "os_version": "4.1.2", "device": "Huawei Y301A2", "device_type": "Mobile", "touch_capable": true, "browser": "Android", "browser_version": "4.1.2", "document_encoding": "UTF-8", "user_language": "en-us", "city": "Mandeville", "country": "United States", "region": "Louisiana", "url": "https://vwo.com/free- trial", "query_params": [], "direct_traffic": true, "search_traffic": false, "email_traffic": false, "returning_visitor": false, "converted_goals": [...], ... } How we use Elasticsearch "converted_goals": [ { "id": 2, "facet_term": "5_2", "conversion_time": "2014-07-09T23:32:41" }, { "id": 6, "facet_term": "5_6", "conversion_time": "2014-07-09T23:37:04" } ] </li> <li> Alongside Elasticsearch as our primary data store, we use a bunch of other things: RabbitMQ - our central queue which receives all the analytics data and pushes to all the consumers which write to different data stores including Elasticsearch and MySQL. MySQL for storing overall counters of visitors and conversions per goal per variations of every campaign. This serves as a cache in front of Elasticsearch - prevents us from calculating total counts by iterating over all the documents and makes loading of reports faster. Consumers - written in Python, responsible for sanitizing and storing data in Elasticsearch and MySQL. New visitors are inserted as a document in Elasticsearch. Conversions of existing visitors are recorded in the document previously inserted for the visitor that converted using Elasticsearchs Update API (Script Updates). Analytics API Server - written in Python using Flask, Gevent and Celery Exposes APIs for querying segmented data and for other tasks such as start tracking campaign, flushing campaign data, flushing account data, etc. Provides a custom JSON based Query DSL which makes the Query API easy to consumer. The API server translates this Query DSL to Elasticsearchs DSL. Example: { and: [ { or: [ { city: New Delhi }, { city: Gurgaon } ] }, { not: { device_type: Mobile } } ] } Current Architecture USA West AsiaEuropeUSA East Data Acquisition Servers Central Queue 1 2 3 4 Consumers / Workers Front-end Application Analytics API Server U pdate counters Sync visitors and conversions </li> <li> Elasticsearch scales, only when planned for. Consider the following: Make your data shardable - cannot emphasize enough on this. If you cannot shard your data, then scaling out will always be a problem, especially with time-series data as it always grows. There are options like user and time based indices. You may shard according to something else. Find what works for you. Use routing to scale reads. Without routing, queries will hit all the shards to find lesser number of documents out of total documents per shard (difficult to find needle in a larger haystack). If you have a lot of shards, then ES will not return unless response from all the shards have arrived and aggregated at the node that received the request. Avoid hotspots because of routing. Sometimes some shards can have a lot more data as compared to rest of the shards. Use Bulk API for the right things - updating or deleting large number of documents on adhoc basis, bulk indexing from another source, etc. Increase the number of shards per index for data distribution but keep it sane if you are creating too many indices (like per day) as shards are resource hungry. Increase replica count to get higher search throughput. Plan for Scaling </li> <li> Elasticsearch does not have ACL - important if you are dealing with user data. There are existing 3rd party plugins for ACL. In our opinion, run Elasticsearch behind Nginx (or Apache) and let Nginx take care of ACL. This can be easily achieved using Nginx + Lua. You may use something equivalent. Have dedicated Master nodes - these will ensure that Elasticsearchs cluster management does not stop (important for HA). Master-only nodes can run on relatively small machines as compared to Data nodes. Disable deleting of indices using wildcards or _all to avoid the most obvious disaster. Spend some time with the JVM. Monitor resource consumption, especially memory and see which Garbage Collector is working the best for you. For us, G1GC worked better than CMS due to high indexing rate requirement. Consider using Doc Values - major advantage is that it takes off memory management out of JVM and let the kernel do the memory management for disk cache. Use the Snapshot API and prepare to use Restore API, hoping you never really have to. Consider rolling restarts with Optimizing indices before restart. Ops - What We Learned </li> </ul>