cassandra_presentation_final
- 1. Patrick White, Gianfranco Scipioni Michael Greer, Violetta Vylegzhanina Shashank Shekhar, Nathan Walker
- 2. Outline •Introduction •Background •Use Cases •Data Model & Query Language •Architecture •Conclusion
- 4. • Open-source database management system (DBMS) • Several key features of Cassandra differentiate it from other similar systems What is Cassandra?
- 5. The ideal database foundation for today’s modern applications “Apache Cassandra is renowned in the industry as the only NoSQL solution that can accommodate the complex requirement of today’s modern line-of-business applications. It’s architected from the ground up for real-time enterprise databases that require vast scalability, high-velocity performance, flexible schema design and continuous availability.” -DataStax
- 6. History of Cassandra ● Cassandra was created to power the Facebook Inbox Search ● Facebook open-sourced Cassandra in 2008 and became an Apache Incubator project ● In 2010, Cassandra graduated to a top-level project, regular update and releases followed
- 7. Motivation and Function ● Designed to handle large amount of data across multiple servers ● There is a lot of unorganized data out there ● Easy to implement and deploy ● Mimics traditional relational database systems, but with triggers and lightweight transactions ● Raw, simple data structures
- 8. Availability “There is no such thing as standby infrastructure: there is stuff you always use and stuff that won’t work when you need it.” -Ben Black, Founder, Boundary
- 9. General Design Features Emphasis on performance over analysis ● Still has support for analysis tools such as Hadoop Organization ● Rows are organized into tables ● First component of a table’s primary key is the partition key ● Rows are clustered by the remaining columns of the key ● Columns may be indexed separately from the primary key ● Tables may be created, dropped, altered at runtime without blocking queries Language ● CQL (Cassandra Query Language) introduced, similar to SQL (flattened learning curve)
- 10. Peer to Peer Cluster • Decentralized design o Each node has the same role • No single point of failure o Avoids issues of master-slave DBMS’s • No bottlenecking
- 11. Fault Tolerance/Durability Failures happen all the time with multiple nodes ● Hardware Failure ● Bugs ● Operator error ● Power Outage, etc. Solution: Buy cheap, redundant hardware, replicate, maintain consistency
- 12. Fault Tolerance/Durability • Replication o Data is automatically replicated to multiple nodes o Allows failed nodes to be immediately replaced • Distribution of data to multiple data centers o An entire data center can go down without data loss occurring
- 13. Performance • Core architectural designs allow Cassandra to outperform its competitors • Very good read and write throughputs o Consistently ranks as fastest amongst comparable NoSql DBMS’s with large data sets “In terms of scalability, there is a clear winner throughout our experiments. Cassandra achieves the highest throughput for the maximum number of nodes…” - University of Toronto
- 14. Scalability Read and write throughput increase linearly as more machines are added “In terms of scalability, there is a clear winner throughout our experiments. Cassandra achieves the highest throughput for the maximum number of nodes…” - University of Toronto
- 15. Comparisons Apache Cassandra Google Big Table Amazon DynamoDB Storage Type Column Column Key-Value Best Use Write often, read less Designed for large scalability Large database solution Concurrency Control MVCC Locks ACID Characteristics High Availability Partition Tolerance Persistence Consistency High Availability Partition Tolerance Persistence Consistency High Availability Key Point – Cassandra offers a healthy cross between BigTable and Dynamo.
- 17. Netflix • online DVD and Blu-Ray movie retailer • Nielsen study showed that 38% of Americans use or subscribe to Netflix
- 18. Netflix: Why Cassandra ● Using a central SQL database negatively impacted scalability and availability ● International Expansion required Multi- Datacenter solution ● Need for configurable Replication, Consistency, and Resiliency in the face of failure ● Cassandra on AWS offered high levels of scalability and availability Jason Brown, Senior Software Engineer at Netflix
- 19. Spotify • Streaming digital music service • Music for every moment on computer, phone, tablet, and more
- 20. Spotify: Why Cassandra • With more users, scalability problems arised using postgreSQL • With multiple data centers, streaming replication in postgreSQL was problematic o ex: high write volumes • Chose Cassandra o lack of single points of failure o no data loss confidence o Big Table design Axel Liljencrantz, Backend Developer at Spotify
- 21. Spotify: Why Cassandra • Cassandra behaves better in specific use cases: o better replication (especially writes) o better behavior in the presence of networking issues and failures, such as partitions or intermittent glitches o better behavior in certain classes of failure, such as server dies and network links going down
- 22. Hulu • a website and a subscription service offering on-demand streaming video media • ~30 million unique viewers per month
- 23. Hulu: Why Cassandra • need for Availability • need for Scalability • Good Performance • Nearly Linear Scalability • Geo-Replication • Minimal Maintenance Requirements Andres Rangel, Senior Software Engineer at Hulu
- 24. Reasons for Choosing Cassandra • Value availability over consistency • Require high write-throughput • High scalability required • No single point of failure
- 25. CAP Theorem
- 26. •BASE – Basically Available Soft-state Eventual consistency (versus ACID) •If all writers stop (to a key), then all its values (replicas) will converge eventually. •If writes continue, then system always tries to keep converging. –Moving “wave” of updated values lagging behind the latest values sent by clients, but always trying to catch up •Converges when R + W > N –R = # records to read, W = # records to write, N = replication factor •Consistency Levels: –ONE -> R or W is 1 –QUORUM -> R or W is ceiling (N + 1) / 2 –ALL -> R or W is N •If you want to write with Consistency Level of ONE and get the same data when you read, you need to read with Consistency Level of ALL Eventual Consistency
- 28. • Cassandra is a column oriented NoSQL system • Column families: sets of key- value pairs o column family as a table and key-value pairs as a row (using relational database analogy) • A row is a collection of columns labeled with a name Key-Value Model
- 29. Cassandra Row • the value of a row is itself a sequence of key-value pairs • such nested key-value pairs are columns • key = column name • a row must contain at least 1 column
- 31. Column names storing values • key: User ID • column names store tweet ID values • values of all column names are set to “-” (empty byte array) as they are not used
- 32. • A Key Space is a group of column families together. It is only a logical grouping of column families and provides an isolated scope for names Key Space
- 33. Comparing Cassandra (C*) and RDBMS • with RDBMS, a normalized data model is created without considering the exact queries o SQL can return almost anything though Joins • with C*, the data model is designed for specific queries o schema is adjusted as new queries introduced • C*: NO joins, relationships, or foreign keys o a separate table is leveraged per query o data required by multiple tables is denormalized across those tables
- 34. Cassandra Query Language - CQL • creating a keyspace - namespace of tables CREATE KEYSPACE demo WITH replication = {‘class’: ’SimpleStrategy’, replication_factor’: 3}; • to use namespace: USE demo;
- 35. Cassandra Query Language - CQL • creating tables: CREATE TABLE users( CREATE TABLE tweets( email varchar, email varchar, bio varchar, time_posted timestamp, birthday timestamp, tweet varchar, active boolean, PRIMARY KEY (email, time_posted)); PRIMARY KEY (email));
- 36. Cassandra Query Language - CQL • inserting data INSERT INTO users (email, bio, birthday, active) VALUES (‘john.doe@bti360.com’, ‘BT360 Teammate’, 516513600000, true); ○ ** timestamp fields are specified in milliseconds since epoch
- 37. Cassandra Query Language - CQL • querying tables • SELECT expression reads one or more records from Cassandra column family and returns a result-set of rows SELECT * FROM users; SELECT email FROM users WHERE active = true;
- 39. Cassandra Architecture Overview ○ Cassandra was designed with the understanding that system/ hardware failures can and do occur ○ Peer-to-peer, distributed system ○ All nodes are the same ○ Data partitioned among all nodes in the cluster ○ Custom data replication to ensure fault tolerance ○ Read/Write-anywhere design ○ Google BigTable - data model ○ Column Families ○ Memtables ○ SSTables ○ Amazon Dynamo - distributed systems technologies ○ Consistent hashing ○ Partitioning ○ Replication ○ One-hop routing
- 40. Transparent Elasticity Nodes can be added and removed from Cassandra online, with no downtime being experienced. 1 2 3 4 5 6 1 7 10 4 2 3 5 6 8 9 11 12
- 41. Transparent Scalability Addition of Cassandra nodes increases performance linearly and ability to manage TB’s-PB’s of data. 1 2 3 4 5 6 1 7 10 4 2 3 5 6 8 9 11 12 Performance throughput = N Performance throughput = N x 2
- 42. High Availability Cassandra, with its peer-to-peer architecture has no single point of failure.
- 43. Multi-Geography/Zone Aware Cassandra allows a single logical database to span 1-N datacenters that are geographically dispersed. Also supports a hybrid on- premise/Cloud implementation.
- 44. Data Redundancy Cassandra allows for customizable data redundancy so that data is completely protected. Also supports rack awareness (data can be replicated between different racks to guard against machine/rack failures). uses ‘Zookeeper’ to choose a leader which tells nodes the range they are replicas for
- 45. • Nodes are logically structured in Ring Topology. • Hashed value of key associated with data partition is used to assign it to a node in the ring. • Hashing rounds off after certain value to support ring structure. • Lightly loaded nodes moves position to alleviate highly loaded nodes. Partitioning
- 46. 0 1 1/2 F E D C B A N=3 h(key2) h(key1) Partitioning & Replication
- 47. • Used to discover location and state information about the other nodes participating in a Cassandra cluster • Network Communication protocols inspired for real life rumor spreading. • Periodic, Pairwise, inter-node communication. • Low frequency communication ensures low cost. • Random selection of peers. • Example – Node A wish to search for pattern in data – Round 1 – Node A searches locally and then gossips with node B. – Round 2 – Node A,B gossips with C and D. – Round 3 – Nodes A,B,C and D gossips with 4 other nodes …… • Round by round doubling makes protocol very robust. Gossip Protocols
- 48. • Gossip process tracks heartbeats from other nodes both directly and indirectly • Node Fail state is given by variable Φ – tells how likely a node might fail (suspicion level) instead of simple binary value (up/down). • This type of system is known as Accrual Failure Detector • Takes into account network conditions, workload, or other conditions that might affect perceived heartbeat rate • A threshold for Φ tells is used to decide if a node is dead • If node is correct, phi will be constant set by application. Generally Φ(t) = 0 Failure Detection
- 49. Write Operation Stages •Logging data in the commit log •Writing data to the memtable •Flushing data from the memtable •Storing data on disk in SSTables
- 50. Write Operations • Commit Log – First place a write is recorded – Crash recovery mechanism – Write not successful until recorded in commit log – Once recorded in commit log, data is written to Memtable • Memtable – Data structure in memory – Once memtable size reaches a threshold, it is flushed (appended) to SSTable – Several may exist at once (1 current, any others waiting to be flushed) – First place read operations look for data • SSTable – Kept on disk – Immutable once written – Periodically compacted for performance
- 51. Write Operations
- 52. Consistency •Read Consistency –Number of nodes that must agree before read request returns –ONE to ALL •Write Consistency –Number of nodes that must be updated before a write is considered successful –ANY to ALL –At ANY, a hinted handoff is all that is needed to return. •QUORUM –Commonly used middle-ground consistency level –Defined as (replication_factor / 2) + 1
- 53. Write Consistency (ONE) 0 Node 1 Node 2 Node 3 Node 4 Node 5 Node 6 replication_factor = 3 R1 R2 R3 Client INSERT INTO table (column1, …) VALUES (value1, …) USING CONSISTENCY ONE
- 54. Write Consistency (QUORUM) 0 Node 1 Node 2 Node 3 Node 4 Node 5 Node 6 replication_factor = 3 R1 R2 R3 Client INSERT INTO table (column1, …) VALUES (value1, …) USING CONSISTENCY QUORUM
- 55. Write Operations: Hinted Handoff • Write intended for a node that’s offline • An online node, processing the request, makes a note to carry out the write once the node comes back online.
- 56. Hinted Handoff 0 Node 1 Node 2 Node 3 Node 4 Node 5 Node 6 replication_factor = 3 and hinted_handoff_enabled = true R1 R2 R3 Client INSERT INTO table (column1, …) VALUES (value1, …) USING CONSISTENCY ANY Write locally: system.hints Note: Doesn’t not count toward consistency level (except ANY)
- 57. Delete Operations • Tombstones – On delete request, records are marked for deletion. – Similar to “Recycle Bin.” – Data is actually deleted on major compaction or configurable timer
- 58. Compaction • Compaction runs periodically to merge multiple SSTables – Reclaims space – Creates new index – Merges keys – Combines columns – Discards tombstones – Improves performance by minimizing disk seeks • Two types – Major – Read-only
- 59. Compaction
- 60. Anti-Entropy • Ensures synchronization of data across nodes • Compares data checksums against neighboring nodes • Uses Merkle trees (hash trees) • Snapshot of data sent to neighboring nodes • Created and broadcasted on every major compaction • If two nodes take snapshots within TREE_STORE_TIMEOUT of each other, snapshots are compared and data is synced.
- 61. Merkle Tree
- 62. Read Operations • Read Repair – On read, nodes are queried until the number of nodes which respond with the most recent value meet a specified consistency level from ONE to ALL. – If the consistency level is not met, nodes are updated with the most recent value which is then returned. – If the consistency level is met, the value is returned and any nodes that reported old values are then updated.
- 63. Read Repair 0 Node 1 Node 2 Node 3 Node 4 Node 5 Node 6 R1 R2 R3 Client SELECT * FROM table USING CONSISTENCY ONE replication_factor = 3
- 64. Read Operations: Bloom Filters •Bloom filters provide a fast way of checking if a value is not in a set.
- 65. Read Memory Disk Bloom Filter Key Cache Partition Summary Compression Offsets Partition Index Data Cache Hit Cache Miss = Off-heap key_cache_size_in_mb > 0 index_interval = 128 (default)
- 67. • perfect for time-series data • high performance • Decentralization • nearly linear scalability • replication support • no single points of failure • MapReduce support Cassandra Advantages
- 68. Cassandra Weaknesses ● no referential integrity ○ no concept of JOIN ● querying options for retrieving data are limited ● sorting data is a design decision ○ no GROUP BY ● no support for atomic operations ○ if operation fails, changes can still occur ● first think about queries, then about data model
- 69. Cassandra: Points to Consider ● Cassandra is designed as a distributed database management system ○ use it when you have a lot of data spread across multiple servers ● Cassandra write performance is always excellent, but read performance depends on write patterns ○ it is important to spend enough time to design proper schema around the query pattern ● having a high-level understanding of some internals is a plus ○ ensures a design of a strong application built atop Cassandra
- 70. Questions?
- 71. References • Lakshman, Avinash, and Prashant Malik. "Cassandra: a decentralized structured storage system." ACM SIGOPS Operating Systems Review 44.2 (2010): 35-40. • Hewitt, Eben. Cassandra: the definitive guide. O'Reilly Media, 2010. • http://www.datastax.com/documentation/cassandra/2.0/cassandra/architecture/a rchitectureTOC.html • http://www.slideshare.net/planetcassandra/a-deep-dive-into-understanding- apache-cassandra • http://www.slideshare.net/DataStax/evaluating-apache-cassandra-as-a-cloud- database • http://planetcassandra.org/functional-use-cases/ • http://marsmedia.info/en/cassandra-pros-cons-and-model.php • http://www.slideshare.net/adrianco/migrating-netflix-from-oracle-to-global- cassandra • http://wiki.apache.org/cassandra/CassandraLimitations