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The inherent complexity of stream processing

The document discusses approaches for computing unique visitors to web pages over time ranges while dealing with changing user ID mappings. Initially, three approaches are presented using a key-value store: storing user IDs in sets indexed by URL and hour bucket (Approach 1), using HyperLogLogs for more efficient storage (Approach 2), and storing at multiple granularities to reduce lookups (Approach 3). The problem is made harder by the presence of "equiv" records that map one user ID to another. Later approaches try to incrementally normalize user IDs, sample user IDs, or maintain separate indexes. Ultimately, a hybrid approach is proposed using batch computation over the entire dataset to build robust indexes,

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The inherent complexity of stream processing
Easy problem i want this talk to be interactive... going deep into technical details please do not hesitate to jump in with any questions
struct PageView { UserID id, String url, Timestamp timestamp }
Implement: function NumUniqueVisitors( String url, int startHour, int endHour)
Unique visitors over a range A B C A B C D E C D E 1 2 3 4 Hours Pageviews uniques for just hour 1 = 3 uniques for hours 1 and 2 = 3 uniques for 1 to 3 = 5 uniques for 2-4 = 4
Notes: • Not limiting ourselves to current tooling • Reasonable variations of existing tooling are acceptable • Interested in what’s fundamentally possible
Traditional Architectures Application Databases Application Databases Stream processor Queue Synchronous Asynchronous synchronous asynchronous characterized by maintaining state incrementally as data comes in and serving queries off of that same state
Approach #1 • Use Key->Set database • Key = [URL, hour bucket] • Value = Set of UserIDs
Approach #1 • Queries: • Get all sets for all hours in range of query • Union sets together • Compute count of merged set
Approach #1 • Lot of database lookups for large ranges • Potentially a lot of items in sets, so lots of work to merge/count • Database will use a lot of space
Approach #2 Use HyperLogLog
interface HyperLogLog { boolean add(Object o); long size(); HyperLogLog merge(HyperLogLog... otherSets); } 1 KB to estimate size up to 1B with only 2% error
Approach #2 • Use Key->HyperLogLog database • Key = [URL, hour bucket] • Value = HyperLogLog structure it’s not a stretch to imagine a database that can do hyperloglog natively, so updates don’t require fetching the entire set
Approach #2 • Queries: • Get all HyperLogLog structures for all hours in range of query • Merge structures together • Retrieve count from merged structure
Approach #2 • Much more efficient use of storage • Less work at query time • Mild accuracy tradeoff
Approach #2 • Large ranges still require lots of database lookups / work
Approach #3 • Use Key->HyperLogLog database • Key = [URL, bucket, granularity] • Value = HyperLogLog structure it’s not a stretch to imagine a database that can do hyperloglog natively, so updates don’t require fetching the entire set
Approach #3 • Queries: • Compute minimal number of database lookups to satisfy range • Get all HyperLogLog structures in range • Merge structures together • Retrieve count from merged structure
Approach #3 • All benefits of #2 • Minimal number of lookups for any range, so less variation in latency • Minimal increase in storage • Requires more work at write time example: 1 month there are ~720 hours, 30 days, 4 weeks, 1 month... adding all granularities makes 755 stored values total instead of 720 values, only a 4.8% increase in storage
Hard problem
struct Equiv { UserID id1, UserID id2 } struct PageView { UserID id, String url, Timestamp timestamp }
Person A Person B
Implement: function NumUniqueVisitors( String url, int startHour, int endHour) except now userids should be normalized, so if there’s equiv that user only appears once even if under multiple ids
[“foo.com/page1”, 0] [“foo.com/page1”, 1] [“foo.com/page1”, 2] ... [“foo.com/page1”, 1002] {A, B, C} {B} {A, C, D, E} ... {A, B, C, F, Z} equiv can change ANY or ALL buckets in the past
[“foo.com/page1”, 0] [“foo.com/page1”, 1] [“foo.com/page1”, 2] ... [“foo.com/page1”, 1002] {A, B, C} {B} {A, C, D, E} ... {A, B, C, F, Z} A <-> C
Any single equiv could change any bucket
No way to take advantage of HyperLogLog
Approach #1 • [URL, hour] -> Set of PersonIDs • PersonID -> Set of buckets • Indexes to incrementally normalize UserIDs into PersonIDs will get back to incrementally updating userids
Approach #1 • Getting complicated • Large indexes • Operations require a lot of work will get back to incrementally updating userids
Approach #2 • [URL, bucket] -> Set of UserIDs • Like Approach 1, incrementally normalize UserId’s • UserID -> PersonID offload a lot of the work to read time
Approach #2 • Query: • Retrieve all UserID sets for range • Merge sets together • Convert UserIDs -> PersonIDs to produce new set • Get count of new set this is still an insane amount of work at read time overall
Approach #3 • [URL, bucket] -> Set of sampled UserIDs • Like Approaches 1 & 2, incrementally normalize UserId’s • UserID -> PersonID offload a lot of the work to read time
Approach #3 • Query: • Retrieve all UserID sets for range • Merge sets together • Convert UserIDs -> PersonIDs to produce new set • Get count of new set • Divide count by sample rate
Approach #3 if sampled database contains [URL, hour bucket] -> set of sampled user ids
Approach #3 • Sample the user ids using hash sampling • Divide by the sample rate at end to approximate the unique count can’t just do straight random sampling (imagine if only have 4 user ids that visit thousands of times... a sample rate of 50% will have all user ids, and you’ll end up giving the answer of 8)
Approach #3 • Still need complete UserID -> PersonID index • Still requires about 100 lookups into UserID -> PersonID index to resolve queries • Error rate 3-5x worse than HyperLogLog for same space usage • Requires SSD’s for reasonable throughput
Incremental UserID normalization
Attempt 1: • Maintain index from UserID -> PersonID • When receive A <-> B: • Find what they’re each normalized to, and transitively normalize all reachable IDs to “smallest” val
1 <-> 4 1 -> 1 4 -> 1 2 <-> 5 5 -> 2 2 -> 2 5 <-> 3 3 -> 2 4 <-> 5 5 -> 1 2 -> 1 3 -> 1 never gets produced!
Attempt 2: • UserID -> PersonID • PersonID -> Set of UserIDs • When receive A <-> B • Find what they’re each normalized to, and choose one for both to be normalized to • Update all UserID’s in both normalized sets
1 <-> 4 1 -> 1 4 -> 1 1 -> {1, 4} 2 <-> 5 5 -> 2 2 -> 2 2 -> {2, 5} 5 <-> 3 3 -> 2 2 -> {2, 3, 5} 4 <-> 5 5 -> 1 2 -> 1 3 -> 1 1 -> {1, 2, 3, 4, 5}
Challenges • Fault-tolerance / ensuring consistency between indexes • Concurrency challenges if using distributed database to store indexes and computing everything concurrently when receive equivs for 4<->3 and 3<->1 at same time, will need some sort of locking so they don’t step on each other
General challenges with traditional architectures • Redundant storage of information (“denormalization”) • Brittle to human error • Operational challenges of enormous installations of very complex databases e.g. granularities, the 2 indexes for user id normalization... we know it’s a bad idea to store the same thing in multiple places... opens up possibility of them getting out of sync if you don’t handle every case perfectly If you have a bug that accidentally sets the second value of all equivs to 1, you’re in trouble even the version without equivs suffers from these problems
Master Dataset Indexes for uniques over time Stream processor No fully incremental approach works well!
Let’s take a completely different approach!
Some Rough Definitions Complicated: lots of parts
Some Rough Definitions Complex: intertwinement between separate functions
Some Rough Definitions Simple: the opposite of complex
Real World Example 2 functions: produce water of a certain strength, and produce water of a certain temperature faucet on left gives you “hot” and “cold” inputs which each affect BOTH outputs - complex to use faucet on right gives you independent “heat” and “strength” inputs, so SIMPLE to use neither is very complicated
Real World Example #2 recipe... heat it up then cool it down
Real World Example #2 I have to use two devices for the same task!!! temperature control! wouldn’t it be SIMPLER if I had just one device that could regulate temperature from 0 degrees to 500 degrees? then i could have more features, like “450 for 20 minutes then refrigerate” who objects to this? ... talk about how mixing them can create REAL COMPLEXITY... - sometimes you need MORE PIECES to AVOID COMPLEXITY and CREATE SIMPLICITY - we’ll come back to this... this same situation happens in software all the time - people want one tool with a million features... but it turns out these features interact with each other and create COMPLEXITY
ID Name Location ID 1 Sally 3 2 George 1 3 Bob 3 Location ID City State Population 1 NewYork NY 8.2M 2 San Diego CA 1.3M 3 Chicago IL 2.7M Normalized schema Normalization vs Denormalization so just a quick overview of denormalization, here’s a schema that stores user information and location information each is in its own table, and a user’s location is a reference to a row in the location table this is pretty standard relational database stuff now let’s say a really common query is getting the city and state a person lives in to do this you have to join the tables together as part of your query
Join is too expensive, so denormalize... you might find joins are too expensive, they use too many resources
ID Name Location ID City State 1 Sally 3 Chicago IL 2 George 1 NewYork NY 3 Bob 3 Chicago IL Location ID City State Population 1 NewYork NY 8.2M 2 San Diego CA 1.3M 3 Chicago IL 2.7M Denormalized schema so you denormalize the schema for performance you redundantly store the city and state in the users table to make that query faster, cause now it doesn’t require a join now obviously, this sucks. the same data is now stored in multiple places, which we all know is a bad idea whenever you need to change something about a location you need to change it everywhere it’s stored but since people make mistakes, inevitably things become inconsistent but you have no choice, you want to normalize, but you have to denormalize for performance
Complexity between robust data model and query performance you have to choose which one you’re going to suck at
Allow queries to be out of date by hours
Store every Equiv and PageView Master Dataset
Master Dataset Continuously recompute indexes Indexes for uniques over time
Indexes = function(all data)
Equivs PageViews Compute UserID -> PersonID Convert PageView UserIDs to PersonIDs Compute [URL, bucket, granularity] -> HyperLogLog
Equivs PageViews Compute UserID -> PersonID Convert PageView UserIDs to PersonIDs Compute [URL, bucket, granularity] -> HyperLogLog Iterative graph algorithm
Equivs PageViews Compute UserID -> PersonID Convert PageView UserIDs to PersonIDs Compute [URL, bucket, granularity] -> HyperLogLog Join
Equivs PageViews Compute UserID -> PersonID Convert PageView UserIDs to PersonIDs Compute [URL, bucket, granularity] -> HyperLogLog Basic aggregation
Sidenote on tooling • Batch processing systems are tools to implement function(all data) scalably • Implementing this is easy
Person 1 Person 6 UserID normalization
UserID normalization
1 3 4 5 11 2 Initial Graph
1 3 4 5 11 2 Iteration 1
1 3 4 5 11 2 Iteration 2
1 3 4 5 11 2 Iteration 3 / Fixed Point
Conclusions • Easy to understand and implement • Scalable • Concurrency / fault-tolerance easily abstracted away from you • Great query performance
Conclusions • But... always out of date
Absorbed into batch views Not absorbed Now Time Just a small percentage of data!
Master Dataset Batch views New Data Realtime views Query
Get historical buckets from batch views and recent buckets from realtime views
Implementing realtime layer • Isn’t this the exact same problem we faced before we went down the path of batch computation? i hope you are looking at this and asking the question... still have to compute uniques over time and deal with the equivs problem how are we better off than before?
Approach #1 • Use the exact same approach as we did in fully incremental implementation • Query performance only degraded for recent buckets • e.g.,“last month” range computes vast majority of query from efficient batch indexes
Approach #1 • Relatively small number of buckets in realtime layer • So not that much effect on storage costs
Approach #1 • Complexity of realtime layer is softened by existence of batch layer • Batch layer continuously overrides realtime layer, so mistakes are auto-fixed
Approach #1 • Still going to be a lot of work to implement this realtime layer • Recent buckets with lots of uniques will still cause bad query performance • No way to apply recent equivs to batch views without restructuring batch views
Approach #2 • Approximate! • Ignore realtime equivs options for taking different approaches to problem without having to sacrifice too much
UserID -> PersonID (from batch) Approach #2 Pageview Convert UserID to PersonID [URL, bucket] -> HyperLogLog
Approach #2 • Highly efficient • Great performance • Easy to implement
Approach #2 • Only inaccurate for recent equivs • Intuitively, shouldn’t be that much inaccuracy • Should quantify additional error
Approach #2 • Extra inaccuracy is automatically weeded out over time • “Eventual accuracy”
Simplicity
Input: Normalize/denormalize Output: Data model robustness Query performance
Master Dataset Batch views Normalized Robust data model Denormalized Optimized for queries
Normalization problem solved • Maintaining consistency in views easy because defined as function(all data) • Can recompute if anything ever goes wrong
Human fault-tolerance
Complexity of Read/ Write Databases
Black box fallacy people say it does “key/value”, so I can use it when I need key/value operations... and they stop there can’t treat it as a black box, that doesn’t tell the full story
Online compaction • Databases write to write-ahead log before modifying disk and memory indexes • Need to occasionally compact the log and indexes
Memory Disk Key B Key D Key F Value 1 Value 2 Value 3 Value 4 Key A Write A, Value 3 Write F, Value 4 Write-ahead log
Memory Disk Write-ahead log Key B Key D Key F Value 1 Value 2 Value 3 Value 4 Key A Value 5 Write A, Value 3 Write F, Value 4 Write A, Value 5
Memory Disk Write-ahead log Key B Key D Key F Value 1 Value 2 Value 4 Key A Value 5 Compaction
Online compaction • Notorious for causing huge, sudden changes in performance • Machines can seem locked up • Necessitated by random writes • Extremely complex to deal with
Dealing with CAP Application Databases Synchronous
100Replica 1 100Replica 2 Network partition What CAP theorem is