Our journey with druid - from initial research to full production scale
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Here at the Nielsen Marketing Cloud we use druid.io (http://druid.io/) as one of our main data stores, both for simple counts and for approximate count-distinct (DataSketches). It’s been more than a year since we started using it, injecting billions of events each day to multiple druid clusters for different use-cases. In this meet-up, we will share our journey, the challenges we had, the way we overcame them (at least most of them) and the steps we made to optimize the process around Druid to keep the solution cost effective. Before diving into Druid, we will briefly present our data pipeline architecture, starting from the front-end serving system, deployed in number of geo-locations, to a centralized Kafka cluster in the cloud, and give some examples of the different processes that consume from Kafka and feed our different data sources.
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Our journey with druid - from initial research to full production scale
- 1. Our journey with Druid From initial research to full production scale Danny Ruchman + Itai Yaffe Nielsen
- 2. Introduction Danny Ruchman Itai Yaffe ● Software Engineer and team manager ● Focused on big data processing solutions ● Big Data Infrastructure Developer ● Dealing with Big Data challenges for the last 5 years
- 3. Nielsen Marketing Cloud (NMC) ● eXelate was acquired by Nielsen 3 years ago ● A Data company ● Machine learning models for insights ● Business decisions ● Targeting
- 4. Nielsen Marketing Cloud - questions we try to answer ● How many users of a certain profile can we reach Campaign for fancy women sneakers - ● How many hits for a specific web page in a date range
- 8. The need ● Nielsen Marketing Cloud business question ○ How many unique devices we have encountered: ■ over a given date range ■ for a given set of attributes (segments, regions, etc.) ● Find the number of distinct elements in a data stream which may contain repeated elements in real time
- 9. The need
- 10. ● Store everything ● Store only 1 bit per device ○ 10B Devices-1.25 GB/day ○ 10B Devices*80K attributes - 100 TB/day ● Approximate Possible solutions Naive Bit VectorApprox.
- 11. Our journey ● Elasticsearch ○ Indexing data ■ 250 GB of daily data, 10 hours ■ Affect query time ○ Querying ■ Low concurrency ■ Scans on all the shards of the corresponding index
- 12. What we tried ● Preprocessing ● Statistical algorithms (e.g HyperLogLog)
- 13. ● K Minimum Values (KMV) ● Estimate set cardinality ● Supports set-theoretic operations X Y ● ThetaSketch mathematical framework - generalization of KMV X Y ThetaSketch
- 14. KMV intuition
- 15. Number of Std Dev 1 2 Confidence Interval 68.27% 95.45% 16,384 0.78% 1.56% 32,768 0.55% 1.10% 65,536 0.39% 0.78% ThetaSketch error K Value Error %ERROR
- 16. “Very fast highly scalable columnar data-store” DRUID
- 17. Powered by Druid
- 18. Why is it cool? ● Store trillions of events, petabytes of data ● Sub-second analytic queries ● Highly scalable ● Cost effective
- 19. LongSumAggregator 2016-11-15 Timestamp Attribute Device ID 11111 3a4c1f2d84a5c179435c1fea86e6ae02 2016-11-15 11111 3a4c1f2d84a5c179435c1fea86e6ae02 2016-11-15 11111 5dd59f9bd068f802a7c6dd832bf60d02 2016-11-15 22222 5dd59f9bd068f802a7c6dd832bf60d02 2016-11-15 333333 5dd59f9bd068f802a7c6dd832bf60d02 Timestamp Attribute Simple Count 2016-11-15 2016-11-15 2016-11-15 11111 22222 33333 3 1 1 Roll-up - Simple Count
- 20. Roll-up - Count Distinct ThetaSketchAggregator 2016-11-15 Timestamp Attribute Device ID 11111 3a4c1f2d84a5c179435c1fea86e6ae02 2016-11-15 11111 3a4c1f2d84a5c179435c1fea86e6ae02 2016-11-15 11111 5dd59f9bd068f802a7c6dd832bf60d02 2016-11-15 22222 5dd59f9bd068f802a7c6dd832bf60d02 2016-11-15 333333 5dd59f9bd068f802a7c6dd832bf60d02 Timestamp Attribute Count Distinct 2016-11-15 2016-11-15 2016-11-15 11111 22222 33333 2 1 1
- 22. How do we use Druid
- 23. Query performance benchmark Concurrent Queries Avg.ResponseTime Druid . Elasticsearch
- 24. Guidelines and pitfalls ● Setup is not easy
- 25. Guidelines and pitfalls ● Monitoring your system
- 26. Guidelines and pitfalls ● Monitoring your system - important metrics (incomplete list) : ○ Broker query time ○ Historical query time ○ Historical query wait time ○ Pending segments ○ Broker query TTFB ○ ...
- 27. Guidelines and pitfalls ● Monitoring your system
- 28. Guidelines and pitfalls ● Data modeling ○ Reduce the number of intersections ○ Different datasources for different use cases 2016-11-15 2016-11-15 2016-11-15 Timestamp Attribute Count Distinct Timestamp Attribute Region Count Distinct US XXXXXX US Porsche Intent XXXXXX Porsche Intent ... ...... XXXXXX ...
- 29. Guidelines and pitfalls ● Query optimization ○ Combine multiple queries into single query ○ Use filters ○ Use groupBy v2 engine (default since 0.10.0) ○ Use timeseries rather than groupBy queries (where applicable)
- 30. Guidelines and pitfalls ● Batch Ingestion ○ EMR Tuning ■ 140-nodes cluster ● 85% spot instances => ~80% cost reduction ○ Druid input file format - Parquet vs CSV ■ Reduced indexing time by X4 ■ Reduced used storage by X10 ○ Concurrent ingestion tasks (one per EMR cluster and datasource) ■ Set worker select strategy to fillCapacityWithAffinity
- 31. Guidelines and pitfalls ● Batch Ingestion (WIP) ○ Action - pre-aggregating the data in Spark Streaming app ■ Aggregating the data by key ● groupBy().agg() for simple counts ● combineByKey() for distinct count (using the DataSketches packages) Requires setting isInputThetaSketch=true on ingestion task ■ Increased micro-batch interval from 30 minutes to 1 hour ○ Result : ■ # of output records is ~2000X smaller and total size of output files is less than 1%, compared to the previous version ■ 10X less nodes in the EMR cluster running the MapReduce ingestion job
- 32. Guidelines and pitfalls ● Community
- 33. Future work ● Improving accuracy for small set <-> big set intersections ● Improving query performance ○ groupBy V2 ○ NVMe SSDs ○ Switching to timeseries query type where applicable ○ Apply less granular aggregation where applicable (e.g 1 month rather than 1 day) ● Upgrading Druid to 0.11.0 ● Exploring option of tiering of query processing nodes ○ Reporting vs interactive queries ○ Hot vs cold data ● Using SQL interface (experimental) ● Using Lookups (experimental)
- 34. DRUID ES What have we learned? ● Druid is a columnar, time series data-store ● Can store trillions of events and serve analytic queries in sub-second ● Highly-scalable, cost-effective ● Widely used among Big Data companies ● Can be used for : ○ Distinct count (via ThetaSketch) ○ Simple counts ● Setup is not easy ● Ingestion has little effect on query performance (deep storage usage) ● Provides very good visibility ● Improve query performance by carefully designing your data model and building your queries
- 35. QUESTIONS? Join us - https://www.comeet.co/jobs/nielsen/33.000 Big Data Architect Java & Machine Learning Developer Junior Big Data Developer And more...
- 36. THANK YOU!https://www.linkedin.com/in/danny-ruchman-70211a27/ https://www.linkedin.com/in/itaiy/
- 37. Druid vs ES 10TB/day 4 Hours/day 15GB/day 280ms-350ms $55K/month DRUID 250GB/day 10 Hours/day 2.5TB (total) 500ms-6000ms $80K/month ES
Editor's Notes
- Welcome to nielsen marketing cloud offices Thank you for coming to hear about our journey with druid We will try to make it interesting and valuable for you
- Me, 5th year @ NMC Today I lead one of the 2 data teams. I am doing it in the last 2 years and I am loving every minute and I am going to talk about NMC data pipeline loads all our data into our data sources put you in context of druid path Itai, 5th year @ NMC Senior big data engineer One of the engineers who lead the druid research and implementation effort will talk about the druid from idea to production, will give super cool tips for beginners Question - pop in - know -> answer, don’t know - will say it’s a good question with complicated solution, let’s talk after the session
- Nielsen marketing cloud or NMC in short A group inside Nielsen, Born from exelate company that was acquired by Nielsen 3 years ago Nielsen is a data company and so are we and we had strong business relationship until at some point they decided to go for it and acquired exelate Data company meaning Buying and onboarding data into NMC from data providers, customers and Nielsen data We have huge high quality dataset enrich the data using machine learning models in order to create more relevant quality insights categorize and sell according to a need Helping brands to take intelligence business decisions E.g. Targeting in the digital marketing world Meaning help fit ads to viewers For example street sign can fit to a very small % of people who see it vs Online ads that can fit the profile of the individual that sees it More interesting to the user More chances he will click the ad Better ROI for the marketer
- What are the questions we try to answer in NMC that helps our customers to take business decisions ? A lot of questions but to lead to what druid is coming to solveֿ Translating from human problem to technical problem: Uu (distinct) count Simple count
- Few words on NMC data pipeline architecture: Frontend layer: Receives all the online and offline data traffic Bare metal on different data centers 3 us,2 eu ,2 pacific near real time - high throughput/low latency challenges Backend layer Aws Cloud based process all the frontend layer outputs ETL’s - load data to data sources aggregated and raw Applications layer Also in the cloud Variety of apps above all our data sources Web - NMC data configurations (segments, audiences etc) campaign analysis , campaign management tools etc. visualized profile graphs reports
- In each data center the frontend layer is build out of few systems: Data serving - in house gets the external traffic online and offline analyse the event read/write user repository Run algorithms to identify relevant buyers output to kafka Scale of 5B events/day with 200ms SLA while most events are done in few tens of ms’s Modeling and scoring system - in house Scoring and learning Online machines learning algorithms look alike models Cross device models 1.7T models a day Avarage 1100 model execution in a single event Less than 20ms User repository Aerospike 8B active users in us space 3B active users in eu space Everything goes to kafke in each DC and replicated using uReplicator to the centralized kafka in the cloud uReplicator - uber open source that we modified a little to fit our needs, like mirror maker
- Centralizes Kafka cluster in the cloud for all the data from all kafka clusters in all DC’s 50 big brokers 10 topics Deals with 11B events per day Kafka data processing is done mainly with spark streaming Loading sources such as DRUID - real time analytics from the web applications 10B events per day Clustrix - relational DB, aggregated data RDR - data lake 15 TB per day - compressed parquet Snowflake - managed db for DS and analytics purposes Now after presenting the data pipeline itay will go deeper into druid use case and implementation details
- Danny talked about the 2 main use-cases - counting unique users and counting hits (or “simple counts”). The first one is somewhat harder, so this is going to be at the focus of my part of the presentation Past… Mention “cardinality” and “real-time dashboard” Explain the need to union and intersect
- Demo
- Who’s familiar with the count-distinct problem? For the 2 first solutions, we need to store data per device per attribute per day Bit vector - Elastic search /Redis is an example of such system Approximation has a certain error rate
- We tried to introduce new cluster dedicated for indexing only and then use backup and restore to the second cluster This method was very expensive and was partially helpful Tuning for better performance also didn’t help too much The story about the demo when we were at the bar (December 20th, 2016)...
- Preprocessing - Too many combinations - The formula length is not bounded (show some numbers) HyperLogLog -Implementation in ElasticSearch was too slow (done on query time) - Set operations increase the error dramatically
- ThetaSketch is based on KMV Unions and Intersections increase the error The problematic case is intersection of very small set with very big set
- The larger the K the smaller the Error However larger K means more memory & storage needed Demo - http://content.research.neustar.biz/blog/kmv.html
- So we talked about statistical algorithms, which is nice, but we needed a practical solution… OOTB supports ThetaSketch algorithm Open source, written in Java (works for us, as we know Java…) Who’s familiar with Druid?
- Just to give you a sense of where Druid is used in production... http://druid.io/druid-powered.html
- I’ll try to cover all these reasons in the next slides
- Timeseries database - first thing you need to know about Druid Column types : Timestamp Dimensions Metrics Together they comprise a Datasource Agg is done on ingestion time (outcome is much smaller in size) In query time, it’s closer to a key-value search
- We’re just using a different type of aggregator (the ThetaSketch aggregator) to get count distinct, but everything else is essentially the same. The one big difference is multiple ingestions of the same data : For ThetaSketch - not a problem due to the fact is “samples” the data (chooses the K minimal values); Whereas for Sum - we’re going to get wrong numbers (e.g 2X as big if we ingest the data twice) To mitigate it, we’ve added a simple meta-data store to prevent ingesting the same data twice (I’ll discuss it later)
- We have 3 types of processes - ingestion, querying, management All processes are decoupled and scalable Ingestion (real time - e.g from Kafka, batch - talk about deep storage, how data is aggregated in ingestion time) Querying (brokers, historicals, query performance during ingestion vs ES) Lambda architecture (for those who don’t know - it’s “a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch- and stream-processing methods.”)
- Explain the tuple and what is happening during the aggregation Mention it says “ThetaSketchAggregator”, but again - we also use LongSumAggregator for simple counts We ingest a lot more data today than we did in ES (10B events/day in TBs of data vs 250GB in ES) I mentioned our meta-data store earlier - each component in the flow updates that meta-data store, to prevent ingestion of the same data twice
- We can see that while ES response time is exponentially increasing, Druid response time is relatively stable Benchmark using : Druid Cluster : 1x Broker (r3.8xlarge) , 8x Historical (r3.8xlarge) Elasticsearch Cluster : 20 nodes (r3.8xlarge) This is how we use it, now switching to how we got there and the pains...
- Setup is not easy Separate config/servers/tuning Caused the deployment to take a few months Use the Druid recommendation for Production configuration
- Monitoring Your System Druid has built in support for Graphite ( exports many metrics ), so does Spark. We also export metrics to Graphite from our ingestion tasks (written in Python) and from the NMC backend (aesreporter) to provide a complete, end-to-end view of the system. In this example, query time is very high due to a high number of pending segments (i.e segments that are queued to be scanned in order to answer the query)
- http://druid.io/docs/latest/operations/metrics.html Note: Druid is very verbose, can emit a lot of metrics, beware of overwhelming your Graphite server
- Monitoring Your System To avoid having to maintain our own Graphite backend, we use Hosted Graphite (https://www.hostedgraphite.com/), which allows us not only to collect metrics from all the components mentioned earlier, but also to build Grafana dashboards and to get Slack notifications when something unexpected happens (e.g ingestion task failure).
- Data Modeling If using Theta sketch - reduce the number of intersections (show a slide of the old and new data model). In this example, US is a very big set, Porsche intent is (probably) a small set It didn’t solve all use-cases, but it gives you an idea of how you can approach the problem Different datasources - e.g lower accuracy (i.e lower K) for faster queries VS higher accuracy with a bit slower queries
- Combine multiple queries over the REST API (explain why?) There can be billions of rows, so filter the data as part of the query groupBy v2 offers better performance and memory management (e.g generates per-segment results using a fully off-heap map) Switching from groupBy to timeseries query seems to have solved the “io.druid.java.util.common.IAE: Not enough capacity for even one row! Need[1,509,995,528] but have[0].” we had
- EMR tuning (spot instances (80% cost reduction, but it comes with a risk of out-biding and nodes are lost), druid MR prod config) Use Parquet Affinity - use fillCapacityWithAffinity to ingest data from multiple EMR clusters to the same Druid cluster (but different datasources) concurrently, see http://druid.io/docs/latest/configuration/indexing-service.html#affinity
- Why? Ingestion still takes a lot of time and resources There was almost no “penalty” on the Spark Streaming app (with the new version of the app)
- A slide mainly for reference “Breaking” large sets to smaller sets, then use all those sets as part of the query SQL - added in 0.10.0, can be used from the REST API or from JDBC, not ANSI-SQL Lookups - Druid has limited support for joins through query-time lookups. The common use case of query-time lookups is to replace one dimension value (e.g. a String ID) with another value (e.g. a human-readable String value).
- Ingestion has little effect on query + sub-second response for even 100s or 1000s of concurrent queries With Druid and ThetaSketch, we’ve improved our ingestion volume and query performance and concurrency by an order of magnitude with a lesser cost, compared to our old solution (We’ve achieved a more performant, scalable, cost-effective solution) Nice comparison of open-source OLAP systems for Big Data here - https://medium.com/@leventov/comparison-of-the-open-source-olap-systems-for-big-data-clickhouse-druid-and-pinot-8e042a5ed1c7
- Ingestion has little effect on query + sub-second response for even 100s or 1000s of concurrent queries Cost is for the entire solution (Druid cluster, EMR, etc.) With Druid and ThetaSketch, we’ve improved our ingestion volume and query performance and concurrency by an order of magnitude with a lesser cost, compared to our old solution (We’ve achieved a more performant, scalable, cost-effective solution)