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Cassandra
- 1. by developer fordevelopers Lucian Neghina Big Data & Cloud Computing
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- 4. Distributed Architecture Peer toPeerNode & Ring Client coordinator
- 5. Data Distribution &Replication Client coordinator Partitioners: partitions data across the cluster Replication Factor: how many nodes will the data be replicated to - RF <= number of nodes Replication Strategy: determines the replication for each node - SimpleStrategy - NetworkTopologyStrategy Replication Factor: 3
- 6. Turnable Consistency ALL: highestconsistency and the lowest availability EACH_QUORUM: maintain consistency at the same level in each data center QUORUM: strong consistency if you can tolerate some level of failure LOCAL_QUORUM: used in multiple data center clusters and maintain consistency locally ONE, TWO, THREE: checks closest nodes to the coordinator ANY: provides low latency and a guarantee that a write never fails
- 7. Consistency Strong Consistency CL_W +CL_R > Replication Factor reads always reflect the most recent write Eventual Consistency CL_W + CL_R <= Replication Factor If fast write operations are required, but strong consistency is still desired, the write consistency level is lowered to 1, but now read operations have to verify a matched value on all 3 replicas. Writes will be fast, but reads will be slower.
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- 9. Schema Data Type Data structures: -Keyspace Create / Alter / Drop / Use / Describe - Table / Column Families Create / Alter / Drop / Truncate / Describe - Columns Name, Value, Timestamp, TTL (optional) Operations: insert, update, delete Keyspace Table Partition Row
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- 11. Partition Key 24 58 83 Partition Key= State State UserId Name TX 1 John TX 4 Maria TX 5 Daniel State UserId Name NY 3 David NY 6 Roxana NY 7 Robert 24 58
- 12. Clustering Key Data orderinginside a partition State UserId Name TX 1 John TX 2 Igor TX 3 Maria TX 4 Daniel TX 5 Jonathan TX 6 Ruby State UserId Name City TX 1 John Houston TX 2 Igor Dallas TX 3 Maria Austin TX 4 Daniel Austin TX 5 Jonathan Dallas TX 6 Ruby Austin State City Name UserId TX Austin Daniel 4 TX Austin Maria 3 TX Austin Ruby 6 TX Dallas Igor 2 TX Dallas Jonathan 5 TX Houston John 1 Primary key((State),User_id) Primary key((State),User_id) Primary key((State),City,Name,User_id)
- 13. Primary key Primary key((State),City,Name,User_id) Partitionkey Clustering key Simple Primary key Primary key(Column) - partition key Compound Primary key Primary key(Column1, Column2) - (partition key, clustering key) Compound Partitioning key Primary key((Column1, Column2), Column3) - ((partition key), clustering key) Data is split in partitions, identified by PartitionKey. Clustering key = orders data (on disk) inside a partition. Data will be sorted on disk. Combination of Partition Key(s) and Clustering key(s) = Primary Key We can perform either equality “=” or range queries (>,<) on clustering columns Clustering key is optional, you can have Partition with only Partition Key = Single Row Partitions
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- 16. Indexes Partition key Other columns Queriesby partition key are local, all good Scanning all the data Indexes the data held by a given node. Secondary Indexes Server side data denormalization. Materialized Views Client site data denormalize Design tables per query Allow Filtering Local Indexing Distributed Indexing Queries
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- 18. Materialized Views (3.0and later)
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- 20. Fact-Based Model ● Aliceis a user ● Alice is 28 y.o. ● Alice wears a wristband ● A wristband is a sensor ● A wristband records a heart rate ● A heart rate is a measurement
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- 22. Workflow & Queries Q1:Find a user with a known username Q2: Find followers of user Q3: Find sensors owned by a user Q4: Find measurements for a sensor in a data range Q5: Find daily summary of hourly aggregates
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- 25. Limitations No arbitrary WHEREclauses in CQL you predicate can only contain columns specified in your primary key No JOIN construct there is no way to join data across column No native GROUP BY you cannot group identical data No arbitrary ORDER BY clauses order by can be applied to a cluster column No column filtering table columns cannot be filtering without creating the index
- 26. Data definition: ● Keyspace Create,Use, Alter, Drop ● Table Create, Alter, Drop, Truncate ● Index Create, Drop ● Type Create, Alter, Drop ● Trigger, Function, Aggregate Create, Drop Operations Data manipulation: ● Insert - can update existing row ● Update - can create new row ● Delete ● Batch ● Select
- 27. Cassandra Cluster https://github.com/eSolutionsGrup/cassandra-spark-cluster $ cdcassandra-spark-cluster $ docker-compose up -d