Puree through Trillion of clicks in seconds using Interana
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Big Data LA NoSQL Big Data Track. User Behavior analytics engine. Concepts around events, sessions, funnels using Interana Analytics Engine. Operational considerations.
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Puree through Trillion of clicks in seconds using Interana
- 1. Interana Puree through Trillions of clicks in seconds
- 3. Who Am I Big Data Engineer at Interana SQL, Cassandra, Redis, Mongo, SOLR and now Interana jag@interana.com/Github/LinkedIn If u want create a big problem, build a database, if u want to create huge problem, never delete anything
- 4. Data > Opinion Journey ● Three Engineers Lior Abraham, Bobby Johnson and Ann Johnson ● Scuba at Facebook ● Take it to the masses ● Full Stack Solution - UI/API, Ingest and Storage Tier
- 5. Customers
- 6. Use Cases ● Dynamic Session ● Purchase/Ad Funnels ● Continuous deployment - A/B ● Bot Detection Want answers over TRILLIONS of data points in REALTIME
- 8. Concepts Event Stream Event - Actor - Behavior - At Time T, Jack makes purchase Session - Between Login and Logout, inactive time Cohort - Male, 25, California, In-Out-Burger Funnel - Click On Ad => Viewed Item => Add to Cart => Made Purchase => Satisfaction Metric - Equation column from existing, storageless
- 9. Time ordered Event Data Timestamp User (SK) Ad_id (SK) Behaviour Is_Alcoholic (DM) July 1st Jack Beer Clicked on Ad True July 1st Jill Juice Clicked on Add False July 2nd Jack Added To Cart July 3rd Jack Purchase July 5th Jill Added To Cart July 10th Jill Logged Out
- 10. Performance, Performance, Performance ● Columnar Store design for fast scanning ● C++/MMAP ● Pipelining ● Compression
- 11. I/O - keep it near the core
- 12. Sampling ● Lies, Damn Lies and Sampling ● Sampling take advantage of SK <-> Actor relationship ● Confidence depends on the shape of the distributions ● Sample rate is key. Sparse data get tricky across 100’s of shards
- 13. Sampling
- 14. Ingest
- 15. Ingest ● Schemaless, evolving organically ● Transformers and Pipelines ● Pull(S3, Blob, FS) or Push (HTTP) ● Dedupe and Replay friendly.
- 16. Columnar Format
- 17. Operational Approach ● Managed Service - Hosted in AWS/Azure environment ● Coming Soon - Container based solution (AMI), self-server Import as well ● Performance is critical - Sata vs SSD, Tiered Disks, RAM ● Redundancy - currently no live redundancy, use backup and playback
- 18. The Cluster
- 19. Typical Performance Sampled Unsampled Sampled/ Tiered Count * 2s 11s 20s Group/Filte r 2s 17s 10s Session duration 5s 45s 10s Funnel 10s 60s 20s Ex. AWS - 32 * I2.xlarge - 800 GB SSD + 1.6 TB Tiered = 70 TB of storage 2 Trillion Rows * 1000 columns Peak Throughput 500 MB/s Lines 10 M Rows/min Latency 5-20 minutes Query Import
- 20. Explorer
- 21. Music Data Set - 4B Rows ts 1412210780000 userId(sk) 130065 sessionId(sk) 999FAFD51ASD artist Audioslave auth Logged In lastName Brown level free page NextSong song Gasoline
- 22. Demo ● Music Data Set - 4B ● Dashboards ● Explorer ● Funnels
- 23. Thanks For more tech advice come to our blog https://www.interana.com/blog/
Hinweis der Redaktion
- Analytics engine
- Franca lingua - analytics engine
- Started event data to represent satellite ground events in finite state machine 15 years ago. Could fit into single db (10M rows). We generate 10B Rows a day ( 1/1000 day = 86 seconds).
- High dimensional. know the question. Naming things and cache invalidation. How many people have worked in an environment, where u had people use your software or want to. How many people made a feature decisions based on managers decision? And how many have used data to drive there decisions for products?
- Came of stealth. Billions to Trillians. Startups To Enterprise. Consumer to B2B.
- We do both!!! Unauthorized - application Took Funnel users for devices. Sonos funnel, 3 step, first step attempt for action, error, action. Funnel Dropoff. Drill down cohort, matrix. User id and count events, different types of errors.
- Circle - follow link. Is Sparse. Is event based (time variying vs timeseries). Questions :
- Actor is the thing that your are following. Most system only have one or two of these. Franca Lingua of Click Stream/Event Sream
- Sessions are dynamically generated depending on the state. Highlighted are sessions The turn a a set of events into a shard key. Funnels go across sessions. They talk about the population as a whole
- Like cassandra MMAP/MALLOC Pipeling - using caches. Keep data and instructions ocal Compression - focus on streaming data compression vs block level compressions
- Realtime, neartime and batch time
- Why does it matter - It impacts the answer but you save 10x the time producing the answer. Google example. Directionaly. Power Law - Uniform Distribution. Distribution Counts(click). 100 shards, 100 users. Uniform across (i.e. The data distribution is normal). Outliers. Unsampled. Number of Actor Result. Density Actors/shards. Spend Summing to distribution is to wide. Tail distribution/Pareto Distribution. Heavy Ended. In those cases we alert, fully unsampled amount of time. Quality Distribution. Confidence measure. Shard Key. Effectively high availability. Delta-Dira, One User, Scaling or Delta Distribution. Count * event. Delta on check engine light. One user. Hackers behaviour, get 0 back. Check engine light. In Delta Dirac. Impact Scaling---------*-------------------*------
- Ensure all distribution are similar. Uniformily distributed across shards. Sharding
- Default, Continuous window temporal with time. Time Append Write. Ask during demo, how many have dataset on these backings. How much data.. Is it, Gigabyte, Terabyets, PetaBytes.
- Question : Where is your data housed? Import/Export vs Ingest. Formatting. Other things that things struggle with. File/Stream/Custom Protocol(Kafka, et all). More confortable, Unix box command line stuff.
- How many people use Amazon Marketplace. How many use a Virtual solution (.hvi) ? How many just plain old tarball?
- https://demo.interana.com/?dashboard=dashboard-test-2&name=Music%20Dashboard. 1440 minutes a day
- Demographic,, flexible behavior driven cohorts. The music data
- Demographic, old school metal, flexible behavior driven cohorts