Performance - Deepstash
Performance

Performance

  • Fast full-text search - Because it's built on top of Lucene
  • Near real-time indexing - It takes under 1s for newly indexed documents to show up in searches
  • High performance & Fault tolerance - Each index is split into shards, that are distributed and replicated across servers. This enables Elasticsearch to process large volumes of data in parallel and remain available in case of hardware failure.

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MORE IDEAS FROM What is Elasticsearch?

Indexing data

When you add data to Elasticsearch, it is analyzed (parsed, normalized, and enriched), and stored into an inverted index.

The inverted index is a data structure designed for fast full-text searches. It keeps track of all unique words and in which documents each word appears.

It's called inverted because it inverts a document-centric data structure (document -> words) to a keyword-centric data structure (word -> documents).

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Elasticsearch and Lucene

Elasticsearch is a distributed search and analytics engine, built on top of Lucene. It provides a JSON-based REST API and supports sharding, replication, and cluster management, for high availability.

Lucene is a high-performance search engine library, written in Java. It provides fast and memory efficient text indexing (20-30% of the original text size), and powerful search algorithms:

  • Ranked and faceted searching
  • Text highlighting
  • Autocomplete
  • Suggesters ("Did you mean?")
  • Spell checkers
  • Aggregations

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Common use cases
  • Adding search to an application or website
  • Logging and log analytics
  • Application performance monitoring
  • Infrastructure monitoring

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RELATED IDEA

Cloud computing is on-demand access, via the internet, to computing resources—applications, servers (physical servers and virtual servers), data storage, development tools, networking capabilities, and more—hosted at a remote data center managed by a cloud services provider (or CSP). The CSP makes these resources available for a monthly subscription fee or bills them according to usage.

Cloud computing has the following benefits:

  1. Lower IT costs
  2. Improved agility and Time-To-Value
  3. Better scaling.

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Representational state transfer (REST) is a software architectural style that was created to guide the design and development of the architecture for the World Wide Web. REST defines a set of constraints for how the architecture of an Internet-scale distributed hypermedia system, such as the Web, should behave.

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Big data Hadoop
  • Ability to store and process huge amounts of any kind of data, quickly. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things (IoT) , that's a key consideration.
  • Computing power. Hadoop's distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have.
  • Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.
  • Flexibility. Unlike traditional relational databases, you don’t have to preprocess data before storing it. You can store as much data as you want and decide how to use it later. That includes unstructured data like text, images and videos.
  • Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
  • Scalability. You can easily grow your system to handle more data simply by adding nodes. Little administration is required.

MapReduce programming is not a good match for all problems. It’s good for simple information requests and problems that can be divided into independent units, but it's not efficient for iterative and interactive analytic tasks. MapReduce is file-intensive. Because the nodes don’t intercommunicate except through sorts and shuffles, iterative algorithms require multiple map-shuffle/sort-reduce phases to complete. This creates multiple files between MapReduce phases and is inefficient for advanced analytic computing.

There’s a widely acknowledged talent gap. It can be difficult to find entry-level programmers who have sufficient Java skills to be productive with MapReduce. That's one reason distribution providers are racing to put relational (SQL) technology on top of Hadoop. It is much easier to find programmers with SQL skills than MapReduce skills. And, Hadoop administration seems part art and part science, requiring low-level knowledge of operating systems, hardware and Hadoop kernel settings.

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