Apache Spark: core concepts, architecture and internals 03 March 2016 on Spark , scheduling , RDD , DAG , shuffle This post covers core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. We have skipped some parts here. Bring portable devices, which may need to operate disconnected, into the picture and one copy won’t cut it. Prerequisites. Each node will own a particular token range. This is also known as “application partitioning” (not to be confused with database table partitions). https://blog.timescale.com/scaling-partitioning-data-postgresql-10-explained-cd48a712a9a1, There is another part to this, and it relates to the master-slave architecture which means the master is the one that writes and slaves just act as a standby to replicate and distribute reads. Model around your queries. To optimize there is something called periodic compaction that is done where multiple SSTables are combined to a new SSTable file and the older is discarded. Here is a snippet from the net. The relation between PRIMARY Key and PARTITION KEY. In both cases, Cassandra’s sorted immutable SSTables allow for linear reads, few seeks, and few overwrites, maximizing throughput for HDDs and lifespan of SSDs by avoiding write amplification. This is the most essential skill that one needs when doing modeling for Cassandra. Evaluate Confluence today. PARTITION KEY == First Key in PRIMARY KEY, rest are clustering keys, Example 1: PARTITION KEY == PRIMARY KEY== videoid. This is essentially flawed. There are a large number of Cassandra metrics out of which important and relevant metrics can provide a good picture of the system. Cassandra uses the PARTITION COLUMN Key value and feeds it a hash function which tells which of the bucket the row has to be written to. It uses these row key values to distribute data across cluster nodes. DS201: DataStax Enterprise 6 Foundations of Apache Cassandra™ In this course, you will learn the fundamentals of Apache Cassandra™, its distributed architecture, and how data is stored. On the data node, ReadVerbHandler gets the data from CFS.getColumnFamily, CFS.getRangeSlice, or CFS.search for single-row reads, seq scans, and index scans, respectively, and sends it back as a ReadResponse. Cassandra was designed to ful ll the storage needs of the Inbox Search problem. 3. The closest node (as determined by proximity sorting as described above) will be sent a command to perform an actual data read (i.e., return data to the co-ordinating node). Important topics for understanding Cassandra. Since SSTable is a different file and Commit log is a different file and since there is only one arm in a magnetic disk, this is the reason why the main guideline is to configure Commit log in a different disk (not even partition and SStable (data directory)in a separate disk. Master-Master: well if you can make it work then it seems to offer everything, no single point of failure, everyone can work all the time. It also covers CQL (Cassandra Query Language) in depth, as well as covering the Java API for writing Cassandra clients. Suppose there are three nodes in a Cassandra cluster. See the wikipedia article for more. Starting in 1.2, each node may have multiple Tokens. We will discuss two parts here; first, the database design internals that may help you compare between database’s, and second the main intuition behind auto-sharding/auto-scaling in Cassandra, and how to model your data to be aligned to that model for the best performance. (Here is a gentle introduction which seems easier to follow than others (I do not know how it works)). Splitting writes from different individual “modules” in the application (that is, groups of independent tables) to different nodes in the cluster. (More accurately, Oracle RAC or MongoDB Replication Sets are not exactly limited by only one master to write and multiple slaves to read from; but either use a shared storage and multiple masters -slave sets to write and read to, in case of Oracle RAC; and similar in case of MongoDB uses multiple replication sets with each replication set being a master-slave combination, but not using shared storage like Oracle RAC. A single logical database is spread across a cluster of nodes and thus the need to spread data evenly amongst all participating nodes. A snitch determines which datacenters and racks nodes belong to. Apache Cassandra solves many interesting problems to provide a scalable, distributed, fault tolerant database. It has a ring-type architecture, that is, its nodes are logically distributed like a ring. This is one of the reasons that Cassandra does not like frequent Delete. Some classes have misleading names, notably ColumnFamily (which represents a single row, not a table of data) and, prior to 2.0, Table (which was renamed to Keyspace). CREATE TABLE rank_by_year_and_name ( PRIMARY KEY ((race_year, race_name), rank) ); For writes to be distributed and scaled the partition key should be chosen so that it distributes writes in a balanced way across all nodes. Storage engine When performing atomic batches, the mutations are written to the batchlog on two live nodes in the local datacenter. Please see above where I mentioned the practical limits of a pseudo master-slave system like shared disk systems). About Apache Cassandra. If it’s good to minimize the number of partitions that you read from, why not put everything in a single big partition? Cassandra performs very well on both spinning hard drives and solid state disks. Partition key: Cassandra's internal data representation is large rows with a unique key called row key. This course provides an in-depth introduction to working with Cassandra and using it create effective data models, while focusing on the practical aspects of working with C*. To have a good read performance/fast query we need data for a query in one partition read one node.There is a balance between write distribution and read consolidation that you need to achieve, and you need to know your data and query to know that. Database scaling is done via sharding, the key thing is if sharding is automatic or manual. Cassandra’s main feature is to store data on multiple nodes with no single point of failure. One subtle thing about Spanner is that it gets serializability from locks, but it gets external consistency (similar to linearizability) from TrueTime, https://storage.googleapis.com/pub-tools-public-publication-data/pdf/45855.pdf. In general, if you are writing a lot of data to a PostgreSQL table, at some point you’ll need partitioning. Cassandra is designed to handle big data. In Cassandra, nodes in a cluster act as replicas for a given piece of data. The row cache will contain the full partition (storage row), which can be trimmed to match the query. It is not just a Postgres problem, a general google search (below) on this should throw up many problems most such software, Postgres, MySQL, Elastic Search etc. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data. LeveledCompactionStrategy provides stricter guarantees at the price of more compaction i/o; see. The main problem happens when there is an automatic switchover facility for HA when a master dies. You can see how the COMPOSITE PARTITION KEY is modelled so that writes are distributed across nodes and reads for particular state lands in one partition. Every write operation is written to the commit log. The point is, these two goals often conflict, so you’ll need to try to balance them. Note that Delete’s are like updates but with a marker called Tombstone and are deleted during compaction. {"serverDuration": 138, "requestCorrelationId": "50f7bd6f5ac860cb"}, https://issues.apache.org/jira/browse/CASSANDRA-833, http://www.datastax.com/dev/blog/leveled-compaction-in-apache-cassandra, http://www.datastax.com/dev/blog/when-to-use-leveled-compaction, http://www.cs.cornell.edu/home/rvr/papers/flowgossip.pdf, http://www.eecs.harvard.edu/~mdw/papers/seda-sosp01.pdf, http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html, annotated and compared to Apache Cassandra 2.0, https://c.statcounter.com/9397521/0/fe557aad/1/, Configuration file is parsed by DatabaseDescriptor (which also has all the default values, if any), Thrift generates an API interface in Cassandra.java; the implementation is CassandraServer, and CassandraDaemon ties it together (mostly: handling commitlog replay, and setting up the Thrift plumbing), CassandraServer turns thrift requests into the internal equivalents, then StorageProxy does the actual work, then CassandraServer turns the results back into thrift again, CQL requests are compiled and executed through. 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