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NoSQL at Twitter (NoSQL EU 2010)
 

NoSQL at Twitter (NoSQL EU 2010)

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A discussion of the different NoSQL-style datastores in use at Twitter, including Hadoop (with Pig for analysis), HBase, Cassandra, and FlockDB.

A discussion of the different NoSQL-style datastores in use at Twitter, including Hadoop (with Pig for analysis), HBase, Cassandra, and FlockDB.

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NoSQL at Twitter (NoSQL EU 2010) NoSQL at Twitter (NoSQL EU 2010) Presentation Transcript

  • NoSQL at Twitter Kevin Weil -- @kevinweil Analytics Lead, Twitter April 21, 2010 TM
  • Introduction ‣ How We Arrived at NoSQL: A Crash Course ‣ Collecting Data (Scribe) ‣ Storing and Analyzing Data (Hadoop) ‣ Rapid Learning over Big Data (Pig) ‣ And More: Cassandra, HBase, FlockDB
  • My Background ‣ Studied Mathematics and Physics at Harvard, Physics at Stanford ‣ Tropos Networks (city-wide wireless): mesh routing algorithms, GBs of data ‣ Cooliris (web media): Hadoop and Pig for analytics, TBs of data ‣ Twitter: Hadoop, Pig, HBase, Cassandra, machine learning, visualization, social graph analysis, soon to be PBs data
  • Introduction ‣ How We Arrived at NoSQL: A Crash Course ‣ Collecting Data (Scribe) ‣ Storing and Analyzing Data (Hadoop) ‣ Rapid Learning over Big Data (Pig) ‣ And More: Cassandra, HBase, FlockDB
  • Data, Data Everywhere ‣ Twitter users generate a lot of data ‣ Anybody want to guess?
  • Data, Data Everywhere ‣ Twitter users generate a lot of data ‣ Anybody want to guess? ‣ 7 TB/day (2+ PB/yr)
  • Data, Data Everywhere ‣ Twitter users generate a lot of data ‣ Anybody want to guess? ‣ 7 TB/day (2+ PB/yr) ‣ 10,000 CDs/day
  • Data, Data Everywhere ‣ Twitter users generate a lot of data ‣ Anybody want to guess? ‣ 7 TB/day (2+ PB/yr) ‣ 10,000 CDs/day ‣ 5 million floppy disks
  • Data, Data Everywhere ‣ Twitter users generate a lot of data ‣ Anybody want to guess? ‣ 7 TB/day (2+ PB/yr) ‣ 10,000 CDs/day ‣ 5 million floppy disks ‣ 300 GB while I give this talk
  • Data, Data Everywhere ‣ Twitter users generate a lot of data ‣ Anybody want to guess? ‣ 7 TB/day (2+ PB/yr) ‣ 10,000 CDs/day ‣ 5 million floppy disks ‣ 300 GB while I give this talk ‣ And doubling multiple times per year
  • Syslog? ‣ Started with syslog-ng ‣ As our volume grew, it didn’t scale
  • Syslog? ‣ Started with syslog-ng ‣ As our volume grew, it didn’t scale ‣ Resources overwhelmed ‣ Lost data
  • Scribe ‣ Surprise! FB had same problem, built and open-sourced Scribe ‣ Log collection framework over Thrift ‣ You write log lines, with categories ‣ It does the rest
  • Scribe ‣ Runs locally; reliable in network outage FE FE FE
  • Scribe ‣ Runs locally; reliable in network outage ‣ Nodes only know downstream FE FE FE writer; hierarchical, scalable Agg Agg
  • Scribe ‣ Runs locally; reliable in network outage ‣ Nodes only know downstream FE FE FE writer; hierarchical, scalable ‣ Pluggable outputs Agg Agg File HDFS
  • Scribe at Twitter ‣ Solved our problem, opened new vistas ‣ Currently 30 different categories logged from multiple sources ‣ FE: Javascript, Ruby on Rails ‣ Middle tier: Ruby on Rails, Scala ‣ Backend: Scala, Java, C++
  • Scribe at Twitter ‣ We’ve contributed to it as we’ve used it ‣ Improved logging, monitoring, writing to HDFS, compression ‣ Continuing to work with FB on patches ‣ GSoC project! Help make it more awesome. • http://github.com/traviscrawford/scribe • http://wiki.developers.facebook.com/index.php/User:GSoC
  • Introduction ‣ How We Arrived at NoSQL: A Crash Course ‣ Collecting Data (Scribe) ‣ Storing and Analyzing Data (Hadoop) ‣ Rapid Learning over Big Data (Pig) ‣ And More: Cassandra, HBase, FlockDB
  • How do you store 7TB/day? ‣ Single machine? ‣ What’s HD write speed?
  • How do you store 7TB/day? ‣ Single machine? ‣ What’s HD write speed? ‣ ~80 MB/s
  • How do you store 7TB/day? ‣ Single machine? ‣ What’s HD write speed? ‣ ~80 MB/s ‣ 24.3 hours to write 7 TB
  • How do you store 7TB/day? ‣ Single machine? ‣ What’s HD write speed? ‣ ~80 MB/s ‣ 24.3 hours to write 7 TB ‣ Uh oh.
  • Where do I put 7TB/day? ‣ Need a cluster of machines
  • Where do I put 7TB/day? ‣ Need a cluster of machines ‣ ... which adds new layers of complexity
  • Hadoop ‣ Distributed file system ‣ Automatic replication, fault tolerance
  • Hadoop ‣ Distributed file system ‣ Automatic replication, fault tolerance ‣ MapReduce-based parallel computation ‣ Key-value based computation interface allows for wide applicability
  • Hadoop ‣ Open source: top-level Apache project ‣ Scalable: Y! has a 4000 node cluster ‣ Powerful: sorted 1TB of random integers in 62 seconds ‣ Easy packaging: free Cloudera RPMs
  • MapReduce Workflow Inputs Map Shuffle/Sort ‣ Challenge: how many tweets per user, given tweets table? Map Outputs ‣ Input: key=row, value=tweet info Map Reduce ‣ Map: output key=user_id, value=1 Map Reduce ‣ Shuffle: sort by user_id Map Reduce ‣ Reduce: for each user_id, sum Map ‣ Output: user_id, tweet count Map ‣ With 2x machines, runs 2x faster
  • MapReduce Workflow Inputs Map Shuffle/Sort ‣ Challenge: how many tweets per user, given tweets table? Map Outputs ‣ Input: key=row, value=tweet info Map Reduce ‣ Map: output key=user_id, value=1 Map Reduce ‣ Shuffle: sort by user_id Map Reduce ‣ Reduce: for each user_id, sum Map ‣ Output: user_id, tweet count Map ‣ With 2x machines, runs 2x faster
  • MapReduce Workflow Inputs Map Shuffle/Sort ‣ Challenge: how many tweets per user, given tweets table? Map Outputs ‣ Input: key=row, value=tweet info Map Reduce ‣ Map: output key=user_id, value=1 Map Reduce ‣ Shuffle: sort by user_id Map Reduce ‣ Reduce: for each user_id, sum Map ‣ Output: user_id, tweet count Map ‣ With 2x machines, runs 2x faster
  • MapReduce Workflow Inputs Map Shuffle/Sort ‣ Challenge: how many tweets per user, given tweets table? Map Outputs ‣ Input: key=row, value=tweet info Map Reduce ‣ Map: output key=user_id, value=1 Map Reduce ‣ Shuffle: sort by user_id Map Reduce ‣ Reduce: for each user_id, sum Map ‣ Output: user_id, tweet count Map ‣ With 2x machines, runs 2x faster
  • MapReduce Workflow Inputs Map Shuffle/Sort ‣ Challenge: how many tweets per user, given tweets table? Map Outputs ‣ Input: key=row, value=tweet info Map Reduce ‣ Map: output key=user_id, value=1 Map Reduce ‣ Shuffle: sort by user_id Map Reduce ‣ Reduce: for each user_id, sum Map ‣ Output: user_id, tweet count Map ‣ With 2x machines, runs 2x faster
  • MapReduce Workflow Inputs Map Shuffle/Sort ‣ Challenge: how many tweets per user, given tweets table? Map Outputs ‣ Input: key=row, value=tweet info Map Reduce ‣ Map: output key=user_id, value=1 Map Reduce ‣ Shuffle: sort by user_id Map Reduce ‣ Reduce: for each user_id, sum Map ‣ Output: user_id, tweet count Map ‣ With 2x machines, runs 2x faster
  • MapReduce Workflow Inputs Map Shuffle/Sort ‣ Challenge: how many tweets per user, given tweets table? Map Outputs ‣ Input: key=row, value=tweet info Map Reduce ‣ Map: output key=user_id, value=1 Map Reduce ‣ Shuffle: sort by user_id Map Reduce ‣ Reduce: for each user_id, sum Map ‣ Output: user_id, tweet count Map ‣ With 2x machines, runs 2x faster
  • Two Analysis Challenges ‣ 1. Compute friendships in Twitter’s social graph ‣ grep, awk? No way. ‣ Data is in MySQL... self join on an n-billion row table? ‣ n,000,000,000 x n,000,000,000 = ?
  • Two Analysis Challenges ‣ 1. Compute friendships in Twitter’s social graph ‣ grep, awk? No way. ‣ Data is in MySQL... self join on an n-billion row table? ‣ n,000,000,000 x n,000,000,000 = ? ‣ I don’t know either.
  • Two Analysis Challenges ‣ 2. Large-scale grouping and counting? ‣ select count(*) from users? Maybe... ‣ select count(*) from tweets? Uh... ‣ Imagine joining them... ‣ ... and grouping... ‣ ... and sorting...
  • Back to Hadoop ‣ Didn’t we have a cluster of machines?
  • Back to Hadoop ‣ Didn’t we have a cluster of machines?
  • Back to Hadoop ‣ Didn’t we have a cluster of machines? ‣ Hadoop makes it easy to distribute the calculation ‣ Purpose-built for parallel computation ‣ Just a slight mindset adjustment
  • Back to Hadoop ‣ Didn’t we have a cluster of machines? ‣ Hadoop makes it easy to distribute the calculation ‣ Purpose-built for parallel computation ‣ Just a slight mindset adjustment ‣ But a fun and valuable one!
  • Analysis at scale ‣ Now we’re rolling ‣ Count all tweets: 12 billion, 5 minutes ‣ Hit FlockDB in parallel to assemble social graph aggregates ‣ Run pagerank across users to calculate reputations
  • But... ‣ Analysis typically in Java ‣ “I need less Java in my life, not more.”
  • But... ‣ Analysis typically in Java ‣ “I need less Java in my life, not more.” ‣ Single-input, two-stage data flow is rigid
  • But... ‣ Analysis typically in Java ‣ “I need less Java in my life, not more.” ‣ Single-input, two-stage data flow is rigid ‣ Projections, filters: custom code
  • But... ‣ Analysis typically in Java ‣ “I need less Java in my life, not more.” ‣ Single-input, two-stage data flow is rigid ‣ Projections, filters: custom code ‣ Joins are lengthy, error-prone
  • But... ‣ Analysis typically in Java ‣ “I need less Java in my life, not more.” ‣ Single-input, two-stage data flow is rigid ‣ Projections, filters: custom code ‣ Joins are lengthy, error-prone ‣ n-stage jobs hard to manage
  • But... ‣ Analysis typically in Java ‣ “I need less Java in my life, not more.” ‣ Single-input, two-stage data flow is rigid ‣ Projections, filters: custom code ‣ Joins are lengthy, error-prone ‣ n-stage jobs hard to manage ‣ Exploration requires compilation!
  • Introduction ‣ How We Arrived at NoSQL: A Crash Course ‣ Collecting Data (Scribe) ‣ Storing and Analyzing Data (Hadoop) ‣ Rapid Learning over Big Data (Pig) ‣ And More: Cassandra, HBase, FlockDB
  • Pig ‣ High-level language ‣ Transformations on sets of records ‣ Process data one step at a time ‣ Easier than SQL?
  • Why Pig? ‣ Because I bet you can read the following script.
  • A Real Pig Script
  • A Real Pig Script ‣ Now, just for fun... the same calculation in vanilla Hadoop MapReduce.
  • No, seriously.
  • Pig Democratizes Large-scale Data Analysis ‣ The Pig version is: ‣ 5% of the code
  • Pig Democratizes Large-scale Data Analysis ‣ The Pig version is: ‣ 5% of the code ‣ 5% of the time
  • Pig Democratizes Large-scale Data Analysis ‣ The Pig version is: ‣ 5% of the code ‣ 5% of the time ‣ Within 25% of the execution time
  • One Thing I’ve Learned ‣ It’s easy to answer questions ‣ It’s hard to ask the right questions
  • One Thing I’ve Learned ‣ It’s easy to answer questions ‣ It’s hard to ask the right questions ‣ Value the system that promotes innovation, iteration
  • One Thing I’ve Learned ‣ It’s easy to answer questions ‣ It’s hard to ask the right questions ‣ Value the system that promotes innovation, iteration ‣ More minds contributing = more value from your data
  • The Hadoop Ecosystem at Twitter ‣ Running Cloudera’s free distro, CDH2 and Hadoop 0.20.1
  • The Hadoop Ecosystem at Twitter ‣ Running Cloudera’s free distro, CDH2 and Hadoop 0.20.1 ‣ Heavily modified Scribe writing LZO-compressed to HDFS ‣ LZO: fast, splittable compression, ideal for HDFS* ‣ * http://www.github.com/kevinweil/hadoop-lzo ‣
  • The Hadoop Ecosystem at Twitter ‣ Running Cloudera’s free distro, CDH2 and Hadoop 0.20.1 ‣ Heavily modified Scribe writing LZO-compressed to HDFS ‣ LZO: fast, splittable compression, ideal for HDFS* ‣ Data either as flat files (logs) or in protocol buffer format (newer logs, structured data, etc) ‣ Libs for reading/writing/more open-sourced as elephant-bird** ‣ * http://www.github.com/kevinweil/hadoop-lzo ‣ ** http://www.github.com/kevinweil/elephant-bird
  • The Hadoop Ecosystem at Twitter ‣ Running Cloudera’s free distro, CDH2 and Hadoop 0.20.1 ‣ Heavily modified Scribe writing LZO-compressed to HDFS ‣ LZO: fast, splittable compression, ideal for HDFS* ‣ Data either as flat files (logs) or in protocol buffer format (newer logs, structured data, etc) ‣ Libs for reading/writing/more open-sourced as elephant-bird** ‣ Some Java-based MapReduce, a little Hadoop streaming ‣ * http://www.github.com/kevinweil/hadoop-lzo ‣ ** http://www.github.com/kevinweil/elephant-bird
  • The Hadoop Ecosystem at Twitter ‣ Running Cloudera’s free distro, CDH2 and Hadoop 0.20.1 ‣ Heavily modified Scribe writing LZO-compressed to HDFS ‣ LZO: fast, splittable compression, ideal for HDFS* ‣ Data either as flat files (logs) or in protocol buffer format (newer logs, structured data, etc) ‣ Libs for reading/writing/more open-sourced as elephant-bird** ‣ Some Java-based MapReduce, some HBase, Hadoop streaming ‣ Most analysis, and most interesting analyses, done in Pig ‣ * http://www.github.com/kevinweil/hadoop-lzo ‣ ** http://www.github.com/kevinweil/elephant-bird
  • Data? ‣ Semi-structured: apache logs (search, .com, mobile), search query logs, RoR logs, mysql query logs, A/B testing logs, signup flow logging, and on...
  • Data? ‣ Semi-structured: apache logs (search, .com, mobile), search query logs, RoR logs, mysql query logs, A/B testing logs, signup flow logging, and on... ‣ Structured: tweets, users, blocks, phones, favorites, saved searches, retweets, geo, authentications, sms, 3rd party clients, followings
  • Data? ‣ Semi-structured: apache logs (search, .com, mobile), search query logs, RoR logs, mysql query logs, A/B testing logs, signup flow logging, and on... ‣ Structured: tweets, users, blocks, phones, favorites, saved searches, retweets, geo, authentications, sms, 3rd party clients, followings ‣ Entangled: the social graph
  • So what do we do with it?
  • Counting Big Data ‣ standard counts, min, max, std dev ‣ How many requests do we serve in a day?
  • Counting Big Data ‣ standard counts, min, max, std dev ‣ How many requests do we serve in a day? ‣ What is the average latency? 95% latency? ‣
  • Counting Big Data ‣ standard counts, min, max, std dev ‣ How many requests do we serve in a day? ‣ What is the average latency? 95% latency? ‣ Group by response code. What is the hourly distribution? ‣
  • Counting Big Data ‣ standard counts, min, max, std dev ‣ How many requests do we serve in a day? ‣ What is the average latency? 95% latency? ‣ Group by response code. What is the hourly distribution? ‣ How many searches happen each day on Twitter? ‣
  • Counting Big Data ‣ standard counts, min, max, std dev ‣ How many requests do we serve in a day? ‣ What is the average latency? 95% latency? ‣ Group by response code. What is the hourly distribution? ‣ How many searches happen each day on Twitter? ‣ How many unique queries, how many unique users? ‣
  • Counting Big Data ‣ standard counts, min, max, std dev ‣ How many requests do we serve in a day? ‣ What is the average latency? 95% latency? ‣ Group by response code. What is the hourly distribution? ‣ How many searches happen each day on Twitter? ‣ How many unique queries, how many unique users? ‣ What is their geographic distribution?
  • Counting Big Data ‣ Where are users querying from? The API, the front page, their profile page, etc? ‣
  • Correlating Big Data ‣ probabilities, covariance, influence ‣ How does usage differ for mobile users?
  • Correlating Big Data ‣ probabilities, covariance, influence ‣ How does usage differ for mobile users? ‣ How about for users with 3rd party desktop clients?
  • Correlating Big Data ‣ probabilities, covariance, influence ‣ How does usage differ for mobile users? ‣ How about for users with 3rd party desktop clients? ‣ Cohort analyses
  • Correlating Big Data ‣ probabilities, covariance, influence ‣ How does usage differ for mobile users? ‣ How about for users with 3rd party desktop clients? ‣ Cohort analyses ‣ Site problems: what goes wrong at the same time?
  • Correlating Big Data ‣ probabilities, covariance, influence ‣ How does usage differ for mobile users? ‣ How about for users with 3rd party desktop clients? ‣ Cohort analyses ‣ Site problems: what goes wrong at the same time? ‣ Which features get users hooked?
  • Correlating Big Data ‣ probabilities, covariance, influence ‣ How does usage differ for mobile users? ‣ How about for users with 3rd party desktop clients? ‣ Cohort analyses ‣ Site problems: what goes wrong at the same time? ‣ Which features get users hooked? ‣ Which features do successful users use often?
  • Correlating Big Data ‣ probabilities, covariance, influence ‣ How does usage differ for mobile users? ‣ How about for users with 3rd party desktop clients? ‣ Cohort analyses ‣ Site problems: what goes wrong at the same time? ‣ Which features get users hooked? ‣ Which features do successful users use often? ‣ Search corrections, search suggestions
  • Correlating Big Data ‣ probabilities, covariance, influence ‣ How does usage differ for mobile users? ‣ How about for users with 3rd party desktop clients? ‣ Cohort analyses ‣ Site problems: what goes wrong at the same time? ‣ Which features get users hooked? ‣ Which features do successful users use often? ‣ Search corrections, search suggestions ‣ A/B testing
  • Correlating Big Data ‣ What is the correlation between users with registered phones and users that tweet?
  • Research on Big Data ‣ prediction, graph analysis, natural language ‣ What can we tell about a user from their tweets?
  • Research on Big Data ‣ prediction, graph analysis, natural language ‣ What can we tell about a user from their tweets? ‣ From the tweets of those they follow?
  • Research on Big Data ‣ prediction, graph analysis, natural language ‣ What can we tell about a user from their tweets? ‣ From the tweets of those they follow? ‣ From the tweets of their followers?
  • Research on Big Data ‣ prediction, graph analysis, natural language ‣ What can we tell about a user from their tweets? ‣ From the tweets of those they follow? ‣ From the tweets of their followers? ‣ From the ratio of followers/following?
  • Research on Big Data ‣ prediction, graph analysis, natural language ‣ What can we tell about a user from their tweets? ‣ From the tweets of those they follow? ‣ From the tweets of their followers? ‣ From the ratio of followers/following? ‣ What graph structures lead to successful networks?
  • Research on Big Data ‣ prediction, graph analysis, natural language ‣ What can we tell about a user from their tweets? ‣ From the tweets of those they follow? ‣ From the tweets of their followers? ‣ From the ratio of followers/following? ‣ What graph structures lead to successful networks? ‣ User reputation
  • Research on Big Data ‣ prediction, graph analysis, natural language ‣ Sentiment analysis
  • Research on Big Data ‣ prediction, graph analysis, natural language ‣ Sentiment analysis ‣ What features get a tweet retweeted?
  • Research on Big Data ‣ prediction, graph analysis, natural language ‣ Sentiment analysis ‣ What features get a tweet retweeted? ‣ How deep is the corresponding retweet tree?
  • Research on Big Data ‣ prediction, graph analysis, natural language ‣ Sentiment analysis ‣ What features get a tweet retweeted? ‣ How deep is the corresponding retweet tree? ‣ Long-term duplicate detection
  • Research on Big Data ‣ prediction, graph analysis, natural language ‣ Sentiment analysis ‣ What features get a tweet retweeted? ‣ How deep is the corresponding retweet tree? ‣ Long-term duplicate detection ‣ Machine learning
  • Research on Big Data ‣ prediction, graph analysis, natural language ‣ Sentiment analysis ‣ What features get a tweet retweeted? ‣ How deep is the corresponding retweet tree? ‣ Long-term duplicate detection ‣ Machine learning ‣ Language detection
  • Research on Big Data ‣ prediction, graph analysis, natural language ‣ Sentiment analysis ‣ What features get a tweet retweeted? ‣ How deep is the corresponding retweet tree? ‣ Long-term duplicate detection ‣ Machine learning ‣ Language detection ‣ ... the list goes on.
  • Research on Big Data ‣ How well can we detect bots and other non-human tweeters?
  • Introduction ‣ How We Arrived at NoSQL: A Crash Course ‣ Collecting Data (Scribe) ‣ Storing and Analyzing Data (Hadoop) ‣ Rapid Learning over Big Data (Pig) ‣ And More: Cassandra, HBase, FlockDB
  • HBase ‣ BigTable clone on top of HDFS ‣ Distributed, column-oriented, no datatypes ‣ Unlike the rest of HDFS, designed for low-latency ‣ Importantly, data is mutable
  • HBase at Twitter ‣ We began building real products based on Hadoop ‣ People search
  • HBase at Twitter ‣ We began building real products based on Hadoop ‣ People search ‣ Old version: offline process on a single node
  • HBase at Twitter ‣ We began building real products based on Hadoop ‣ People search ‣ Old version: offline process on a single node ‣ New version: complex user calculations, hit extra services in real time, custom indexing
  • HBase at Twitter ‣ We began building real products based on Hadoop ‣ People search ‣ Old version: offline process on a single node ‣ New version: complex user calculations, hit extra services in real time, custom indexing ‣ Underlying data is mutable ‣ Mutable layer on top of HDFS --> HBase
  • People Search ‣ Import user data into HBase
  • People Search ‣ Import user data into HBase ‣ Periodic MapReduce job reading from HBase ‣ Hits FlockDB, multiple other internal services in mapper ‣ Custom partitioning
  • People Search ‣ Import user data into HBase ‣ Periodic MapReduce job reading from HBase ‣ Hits FlockDB, multiple other internal services in mapper ‣ Custom partitioning ‣ Data sucked across to sharded, replicated, horizontally scalable, in-memory, low-latency Scala service ‣ Build a trie, do case folding/normalization, suggestions, etc
  • People Search ‣ Import user data into HBase ‣ Periodic MapReduce job reading from HBase ‣ Hits FlockDB, multiple other internal services in mapper ‣ Custom partitioning ‣ Data sucked across to sharded, replicated, horizontally scalable, in-memory, low-latency Scala service ‣ Build a trie, do case folding/normalization, suggestions, etc ‣ See http://www.slideshare.net/al3x/building-distributed-systems- in-scala for more
  • HBase ‣ More products now being built on top of it ‣ Flexible, easy to connect to MapReduce/Pig
  • HBase vs Cassandra ‣ “Their origins reveal their strengths and weaknesses”
  • HBase vs Cassandra ‣ “Their origins reveal their strengths and weaknesses” ‣ HBase built on top of batch-oriented system, not low latency
  • HBase vs Cassandra ‣ “Their origins reveal their strengths and weaknesses” ‣ HBase built on top of batch-oriented system, not low latency ‣ Cassandra built from ground up for low latency
  • HBase vs Cassandra ‣ “Their origins reveal their strengths and weaknesses” ‣ HBase built on top of batch-oriented system, not low latency ‣ Cassandra built from ground up for low latency ‣ HBase easy to connect to batch jobs as input and output
  • HBase vs Cassandra ‣ “Their origins reveal their strengths and weaknesses” ‣ HBase built on top of batch-oriented system, not low latency ‣ Cassandra built from ground up for low latency ‣ HBase easy to connect to batch jobs as input and output ‣ Cassandra not so much (but we’re working on it)
  • HBase vs Cassandra ‣ “Their origins reveal their strengths and weaknesses” ‣ HBase built on top of batch-oriented system, not low latency ‣ Cassandra built from ground up for low latency ‣ HBase easy to connect to batch jobs as input and output ‣ Cassandra not so much (but we’re working on it) ‣ HBase has SPOF in the namenode
  • HBase vs Cassandra ‣ Your mileage may vary ‣ At Twitter: HBase for analytics, analysis, dataset generation ‣ Cassandra for online systems
  • HBase vs Cassandra ‣ Your mileage may vary ‣ At Twitter: HBase for analytics, analysis, dataset generation ‣ Cassandra for online systems ‣ As with all NoSQL systems: strengths in different situations
  • FlockDB ‣ Realtime, distributed social graph store ‣ NOT optimized for data mining ‣ Note: the following slides largely come from @nk’s more complete talk at http://www.slideshare.net/nkallen/ q-con-3770885
  • FlockDB ‣ Realtime, distributed Intersection Temporal social graph store ‣ NOT optimized for data mining ‣ Who follows who (nearly 8 Counts orders of magnitude!) ‣ Intersection/set operations ‣ Cardinality ‣ Temporal index
  • Set operations? ‣ This tweet needs to be delivered to people who follow both @aplusk (4.7M followers) and @foursquare (53K followers)
  • Original solution ‣ MySQL table source_id destination-id ‣ Indices on source_id 20 12 and destination_id 29 12 ‣ Couldn’t handle write 34 16 throughput ‣ Indices too large for RAM
  • Next Try ‣ MySQL still ‣ Denormalized ‣ Byte-packed ‣ Chunked ‣ Still temporally ordered
  • Next Try ‣ Problems ‣ O(n) deletes ‣ Data consistency challenges ‣ Inefficient intersections ‣ All of these manifested strongly for huge users like @aplusk or @lancearmstrong
  • FlockDB ‣ MySQL underneath still (like PNUTS from Y!) ‣ Partitioned by user_id, gizzard handles sharding/partitioning ‣ Edges stored in both directions, indexed by (src, dest) ‣ Denormalized counts stored Forward Backward source_id destination_id updated_at x destination_id source_id updated_at x 20 12 20:50:14 x 12 20 20:50:14 x 20 13 20:51:32 12 32 20:51:32 20 16 12 16
  • FlockDB Timings ‣ Counts: 1ms
  • FlockDB Timings ‣ Counts: 1ms ‣ Temporal Query: 2ms
  • FlockDB Timings ‣ Counts: 1ms ‣ Temporal Query: 2ms ‣ Writes: 1ms for journal, 16ms for durability
  • FlockDB Timings ‣ Counts: 1ms ‣ Temporal Query: 2ms ‣ Writes: 1ms for journal, 16ms for durability ‣ Full walks: 100 edges/ms
  • FlockDB is Open Source ‣ We will maintain a community at ‣ http://www.github.com/twitter/flockdb ‣ http://www.github.com/twitter/gizzard ‣ See Nick Kallen’s QCon talk for more ‣ http://www.slideshare.net/nkallen/q- con-3770885
  • Cassandra ‣ Why Cassandra, for Twitter?
  • Cassandra ‣ Why Cassandra, for Twitter? ‣ Old/current: vertically, horizontally partitioned MySQL
  • Cassandra ‣ Why Cassandra, for Twitter? ‣ Old/current: vertically, horizontally partitioned MySQL ‣ All kinds of caching layers, all application managed
  • Cassandra ‣ Why Cassandra, for Twitter? ‣ Old/current: vertically, horizontally partitioned MySQL ‣ All kinds of caching layers, all application managed ‣ Alter table impossible, leads to bitfields, piggyback tables
  • Cassandra ‣ Why Cassandra, for Twitter? ‣ Old/current: vertically, horizontally partitioned MySQL ‣ All kinds of caching layers, all application managed ‣ Alter table impossible, leads to bitfields, piggyback tables ‣ Hardware intensive, error prone, etc
  • Cassandra ‣ Why Cassandra, for Twitter? ‣ Old/current: vertically, horizontally partitioned MySQL ‣ All kinds of caching layers, all application managed ‣ Alter table impossible, leads to bitfields, piggyback tables ‣ Hardware intensive, error prone, etc ‣ Not to mention, we hit MySQL write limits sometimes
  • Cassandra ‣ Why Cassandra, for Twitter? ‣ Old/current: vertically, horizontally partitioned MySQL ‣ All kinds of caching layers, all application managed ‣ Alter table impossible, leads to bitfields, piggyback tables ‣ Hardware intensive, error prone, etc ‣ Not to mention, we hit MySQL write limits sometimes ‣ First goal: move all tweets to Cassandra
  • Cassandra ‣ Why Cassandra, for Twitter? ‣ Decentralized, fault-tolerant ‣ All kinds of caching layers, all application managed ‣ Alter table impossible, leads to bitfields, piggyback tables ‣ Hardware intensive, error prone, etc ‣ Not to mention, we hit MySQL write limits sometimes ‣ First goal: move all tweets to Cassandra
  • Cassandra ‣ Why Cassandra, for Twitter? ‣ Decentralized, fault-tolerant ‣ All kinds of caching layers, all application managed ‣ Alter table impossible, leads to bitfields, piggyback tables ‣ Hardware intensive, error prone, etc ‣ Not to mention, we hit MySQL write limits sometimes ‣ First goal: move all tweets to Cassandra
  • Cassandra ‣ Why Cassandra, for Twitter? ‣ Decentralized, fault-tolerant ‣ All kinds of caching layers, all application managed ‣ Flexible schema ‣ Hardware intensive, error prone, etc ‣ Not to mention, we hit MySQL write limits sometimes ‣ First goal: move all tweets to Cassandra
  • Cassandra ‣ Why Cassandra, for Twitter? ‣ Decentralized, fault-tolerant ‣ All kinds of caching layers, all application managed ‣ Flexible schema ‣ Elastic ‣ Not to mention, we hit MySQL write limits sometimes ‣ First goal: move all tweets to Cassandra
  • Cassandra ‣ Why Cassandra, for Twitter? ‣ Decentralized, fault-tolerant ‣ All kinds of caching layers, all application managed ‣ Flexible schema ‣ Elastic ‣ High write throughput ‣ First goal: move all tweets to Cassandra
  • Eventually Consistent? ‣ Twitter is already eventually consistent
  • Eventually Consistent? ‣ Twitter is already eventually consistent ‣ Your system may be even worse
  • Eventually Consistent? ‣ Twitter is already eventually consistent ‣ Your system may be even worse ‣ Ryan’s new term: “potential consistency” ‣ Do you have write-through caching? ‣ Do you ever have MySQL replication failures?
  • Eventually Consistent? ‣ Twitter is already eventually consistent ‣ Your system may be even worse ‣ Ryan’s new term: “potential consistency” ‣ Do you have write-through caching? ‣ Do you ever have MySQL replication failures? ‣ There is no automatic consistency repair there, unlike Cassandra
  • Eventually Consistent? ‣ Twitter is already eventually consistent ‣ Your system may be even worse ‣ Ryan’s new term: “potential consistency” ‣ Do you have write-through caching? ‣ Do you ever have MySQL replication failures? ‣ There is no automatic consistency repair there, unlike Cassandra ‣ http://www.slideshare.net/ryansking/scaling- twitter-with-cassandra
  • Rolling out Cassandra ‣ 1. Integrate Cassandra alongside MySQL ‣ 100% reads/writes to MySQL ‣ Dynamic switches for % dark reads/writes to Cassandra
  • Rolling out Cassandra ‣ 1. Integrate Cassandra alongside MySQL ‣ 100% reads/writes to MySQL ‣ Dynamic switches for % dark reads/writes to Cassandra ‣ 2. Turn up traffic to Cassandra
  • Rolling out Cassandra ‣ 1. Integrate Cassandra alongside MySQL ‣ 100% reads/writes to MySQL ‣ Dynamic switches for % dark reads/writes to Cassandra ‣ 2. Turn up traffic to Cassandra ‣ 3. Find something that’s broken, set switch to 0%
  • Rolling out Cassandra ‣ 1. Integrate Cassandra alongside MySQL ‣ 100% reads/writes to MySQL ‣ Dynamic switches for % dark reads/writes to Cassandra ‣ 2. Turn up traffic to Cassandra ‣ 3. Find something that’s broken, set switch to 0% ‣ 4. Fix it
  • Rolling out Cassandra ‣ 1. Integrate Cassandra alongside MySQL ‣ 100% reads/writes to MySQL ‣ Dynamic switches for % dark reads/writes to Cassandra ‣ 2. Turn up traffic to Cassandra ‣ 3. Find something that’s broken, set switch to 0% ‣ 4. Fix it ‣ 5. GOTO 2
  • Cassandra for Realtime Analytics ‣ Starting a project around realtime analytics ‣ Cassandra as the backing store ‣ Using, developing, testing Digg’s atomic incr patches ‣ More soon.
  • That was a lot of slides ‣ Thanks for sticking with me.
  • Questions? Follow me at twitter.com/kevinweil TM