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Scaling Mongodb Pdf

What is HA, how MongoDB achieves HA. • Replica set, components, deployment topologies. • Scaling (Vertical vs Horizontal). • What is a. 1. Scaling MongoDB. Percona Webinar - Wed October 18th AM PDT. Adamo Tonete. MongoDB Senior Service Technical Service Engineer. Thumbnails Document Outline. Find: Previous. Next. Highlight all. Match case. Toggle Sidebar. Find. Previous. Next. Page: Presentation Mode Print.

The DZone Refcard 43 is an introduction to system high availability and scalability terminology and techniques http: The next logical step is the scalable handling of massive data volumes resulting from having these powerful processing capabilities. It also offers an overview of current technologies available in this domain and suggests how to apply them. Data scalability is the ability of a system to store, manipulate,analyze, and otherwise process ever increasing amounts of data without reducing overall system availability, performance,or throughput. Data scalability is achieved by a combination of more powerful processing capabilities and larger but efficient storage mechanisms. Relational and hierarchical databases scale up by adding more processors, more storage, caching systems, and such. Soon they hit either a cost or a practical scalability limit because they are difficult or impossible to scale out. These database management systems are designed as single units that must maintain data integrity, and enforce schema rules to guarantee it. This rigidity is what promotes upward, but not outward, scalability. Data integrity and schemas are suited for handling transactional, normalized, uniform data. They handle unstructured or rapidly evolving data structures with difficulty or exponentially larger costs.

Relational and hierarchical databases scale up by adding more processors, more storage, caching systems, and such. Soon they hit either a cost or a practical scalability limit because they are difficult or impossible to scale out. These database management systems are designed as single units that must maintain data integrity, and enforce schema rules to guarantee it.

This rigidity is what promotes upward, but not outward, scalability. Data integrity and schemas are suited for handling transactional, normalized, uniform data.

They handle unstructured or rapidly evolving data structures with difficulty or exponentially larger costs. There are two general kinds of architectures used for building scalable data systems: Data grids process workloads defined as independent jobs that don't require data sharing among processes.

Storage or network may be shared across all nodes of the grid, but intermediate results have no bearing on other jobs progress or on other nodes in the grid, such as a MapReduce cluster. Data grids expose their functionality through a single API either a Web service or native to the application programming language that abstracts its topology and implementation from its data processing consumer.

NoSQL describes a horizontally scalable, non-relational database with built-in replication support. Applications interact with it through a simple API, and the data is stored in a "schema-free", flat addressing repository, usually as large files or data blocks.

The repository is often a custom file system designed to support NoSQL operations. NoSQL storage is highly replicated a commit doesn't occur until the data is successfully written to at least two separate storage devices and the file systems are optimized for writeonly commits. The storage devices are formatted to handle large blocks 32 MB or mor e.

This list is the implementation counterpart to the data grid areas of application; the data store feeds the computational network and, together, they form the NoSQL database.

It's impossible for a distributed computer system to simultaneously provide all three of these guarantees:. Since only two of these characteristics are guaranteed for any given scalable system, use your functional specification and business SLA service level agreement to determine what your minimum and target goals for CAP are, pick the two that meet your requirements, and proceed to implement the appropriate technology. Some of its main features include:. Documents are stored in two separate collections: The administration tools enable GridFS maintenance.

A MongoDB cluster consists of a master and a slave. The slave may become the master in a fail-over scenario, if necessary. A commit is successful only if the data is written to GridFS and replicated in the slave. MongoDB has a built-in cache that runs directly in the cluster without external requirements.

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BSON is designed to be traversable, lightweight and efficient. The complete BSON specification is available from: Programming in MongoDB requires an active server running the Mongod and the Mongos database daemons see Figure 5 , and a client application that uses one of the languagespecific drivers.

The server will display its status messages to the console unless stdout is redirected elsewhere. This example allocates a database if one doesn't already exist,instantiates a collection on the server, and runs a couple of queries. The PyMongo documentation is available at: A successful insertion into the database results in a valid Object ID.

This is the unique identifier in the database for a given document. When querying the database, a return value will include this attribute:. Users may override this Object ID with any ar gument as long as it's unique, or allow MongoDB to assign one automatically.

If any of these is part of the functional requirements, a SQL database would be better suited for the application.

The GigaSpaces eXtreme Application Platform is a data grid designed to replace traditional application servers. It operates based on an event-processing model where the application dispatches objects to the processing nodes associated with a given data partition. The system may be configured so that data state on the grid may trigger events, or an application may dispatch specific commands imperatively. GigaSpaces XAP also manages all threading and execution aspects of the operation, including thread and connection pools.

The system detects any failed operations in a computational node and automatically rolls back the transaction; it then places it back in the space where another node picks it up to complete processing. The GigaSpaces API abstracts all back-end operations job dispatching, data persistence, and caching and makes it transparent to the application. GigaSpaces XAP may implement distributed computing operations like MapReduce and run them in its nodes, or it may dispatch them for processing to the underlying subsystem if the functionality is available e.

MongoDB, Hadoop. Most NoSQL systems strive to achieve statelessness to increase scalability and data consistency. The Datastore is the main scalability feature of Google App Engine applications. It's not a relational database or a facade for one.

Datastore is a public API for accessing Google's Bigtable high-performance distributed database system. Think of it as a sparse array distributed across multiple servers that also allows an infinite number of columns and rows. Applications may even define new columns "on the fly" The Datastore scales by adding new servers to a cluster; Google provides this functionality without user participation.

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