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Cassandra

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Cassandra - A Decentralized Structured Storage System
Summary

Cassandra is a distributed storage system for managing very large amounts of structured data spread out across many commodity servers, while providing highly available service with no single point of failure. Cassandra aims to run on top of an infrastructure of hundreds of nodes (possibly spread across different data centers). At this scale, small and large components fail continuously. The way Cassandra manages the persistent state in the face of these failures drives the reliability and scalability of the software systems relying on this service.

Cassandra does not support a full relational data model; instead, it provides clients with a simple data model that supports dynamic control over data layout and format. Cassandra system was designed to run on cheap commodity hardware and handle high write throughput while not sacrificing the read efficiency.
Facebook is the largest social media platform which serves hundreds of millions users. There are strict operational requirements on Facebook's platform in terms of performance, reliability and efficiency, and to support continuous growth the platform needs to be highly scalable. Cassandra has techniques to achieve scalability and availability. Cassandra was designed to fulfill the storage needs of the Inbox Search problem. This enables users to search through Facebook inbox.
Main operational requirements were;
• Performance
• Dealing with failures
• Reliability
• Efficiency
• Continuous growth

Related work
Google file system – Distributed file system. It was built for hosting the state of Google's internal applications. GFS uses a simple design with a single master server for hosting the entire metadata and where the data is split into chunks and stored in chunk servers.
Ficus/Coda – Distributed file system. It allows disconnected operations and are resilient to issues such as network partitions and outages
Farsite – Distributed file system. No centralized server.
Bayou – Distributed relational database system
Dynamo – Distributed storage system

Data Model
A table in Cassandra is a distributed multi-dimensional map indexed by a key.
Table – Multidimensional map indexed by key
Columns – Simple nested column families. Column has name, value, timestamp

API

The Cassandra API consists of the following three simple methods.

• insert(table; key; rowMutation)
• get(table; key; columnName)
• delete(table; key; columnName)

System Architecture
In addition to the actual data persistence component, the system needs to have the following characteristics; scalable and robust solutions for load balancing, membership and failure detection, failure recovery, replica synchronization, overload handling, state transfer, concurrency and job scheduling, request marshaling, request routing, system monitoring and alarming, and conjuration management.

The core distributed systems techniques used in Cassandra:
• Partitioning
• Replication
• Membership
• Failure handling
• Scaling

Partitioning
One of the key design features for Cassandra is the ability to scale incrementally. This requires, the ability to dynamically partition the data over the set of nodes (i.e., storage hosts) in the cluster. Simply data distribution across the nodes. Each node in the system is assigned a random value within this space which represents its position on the ring. Each data item identified by a key is assigned to a node by hashing the data item's key to yield its position on the ring, and then walking the ring clockwise to find the first node with a position larger than the item's position. The application species the key and the Cassandra use it to route requests. Thus, each node becomes responsible for the region in the ring between it and its predecessor node on the ring. The principal advantage of consistent hashing is that departure or arrival of a node only affects its immediate neighbors and other nodes remain unaffected.

The basic consistent hashing algorithm has some problems.
• The random position assignment of each node on the ring leads to non-uniform data and load distribution.
• The basic algorithm is oblivious to the heterogeneity in the performance of nodes

Solutions;
• For nodes to get assigned to multiple positions in the circle.
• Analyze load information on the ring and have lightly loaded nodes move on the ring to alleviate heavily loaded nodes.

Replication
Cassandra uses replication to achieve high availability and durability. Each data item is replicated at N hosts, where N is the replication factor configured “per-instance".

Different replication policies;

Cluster membership
It is based on scuttlebutt. The system uses efficient gossip based mechanism. The salient feature of Scuttlebutt is that it has very efficient CPU utilization and very efficient utilization of the gossip channel. It is used for other system control states as well. It uses anti entropy protocol for repair replicated data comparing and reconciling differences.

Bootstrapping and Scaling the cluster
When a node starts for the first time, node selects random token for its position in the ring. The token information is then gossiped through the cluster. Then all bodes get to know their position in the cluster.
When a node needs to join a cluster, it reads its configuration file which contains a list of a few contact points within the cluster. We call these initial contact points, seeds of the cluster.
In Facebook environment the node outages are normal due to failures and maintenance tasks. Failures can occur because of disk failures and bad CPU. But these outages are not caused to permanent departure of the nodes.
When a new node is added into the system, it gets assigned a token such that it can alleviate a heavily loaded node. This results in the new node splitting a range that some other node was previously responsible for. Local Persistence
The Cassandra system relies on the local le system for data persistence.
Write operation

Read operation
Indexes all data on primary key and maintain column indices.

First deployment of Cassandra system within Facebook was for the Inbox search system. The system currently stores TB’s of indexes across a cluster of 600+ cores and 120+ TB of disk space. Performance of the system has been well within our SLA requirements and more applications are in the pipeline to use the Cassandra system as their storage engine. A beta version of Cassandra has been open sourced and can be found from the following link https://code.facebook.com/projects/

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