Download SQL on Big data Technology, Architecture, and Innovation by Sumit Pal PDF

By Sumit Pal

Study a number of advertisement and open resource items that practice SQL on substantial facts structures. you are going to comprehend the architectures of a few of the SQL engines getting used and the way the instruments paintings internally by way of execution, information stream, latency, scalability, functionality, and approach specifications. This publication consolidates in a single position options to the demanding situations linked to the necessities of pace, scalability, and the range of operations wanted for information integration and SQL operations. After discussing the background of the how and why of SQL on massive facts, the ebook presents in-depth perception into the goods, architectures, and techniques taking place during this quickly evolving house. SQL on immense info discusses intimately the thoughts occurring, the services at the horizon, and the way they remedy the problems of functionality and scalability and the facility to deal with various facts varieties. The publication covers how SQL on great info engines are permeating the OLTP, OLAP, and Operational analytics area and the quickly evolving HTAP structures. you are going to study the main points of: Batch Architectures―an realizing of the internals and the way the present Hive engine is outfitted and the way it really is evolving always to help new gains and supply reduce latency on queries Interactive Architectures―an knowing of the way SQL engines are architected to help low latency on huge info units Streaming Architectures―an figuring out of ways SQL engines are architected to aid queries on information in movement utilizing in-memory and lock-free information constructions Operational Architectures―an figuring out of the way SQL engines are architected for transactional and operational platforms to help transactions on tremendous information systems leading edge Architectures―an exploration of the quickly evolving more recent SQL engines on great facts with cutting edge principles and ideas

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Basically available” means that the system guarantees availability. “Soft State” means that the state of the system may change over time, even without input that could result from the eventual consistency model. Eventual consistency is the core concept behind BASE. Maintaining changing data in a cluster-based data-storage system that spans across data centers and is replicated across multiple locations involves latency. A change made in one data center takes a while to propagate to another data center or node.

If one wanted to make Hive work with an unknown format that is still not invented, writing a SerDe for that format would provide an easy way to integrate Hive with that data format. Let’s take an example of how to work with JSON data in a Hive table using JSON SerDe. If we have a file with JSON data as the following: {"Country":"A","Languages":["L1","L2","L3"],"Geography":{"Lat":"Lat1", "Long ":"Long1"},"Demographics":{"Male":"10000", "Female":"12000"}} Let us create a Hive table using JSON SerDe to work with the JSON data in the preceding file.

This is not much different from what occurs in a typical relational database engine when an SQL query is submitted. Logical Plan Generator Parser HQL Semantic Analyzer Physical Plan Generator Logical Optimizer Execution Physical Optimizer Figure 3-2. Hive query execution The overall objective of the series of steps is for the Hive compiler to take a HiveQL query and translate it into one or more MapReduce jobs. The parser will parse the HQL and generate a Parse Tree, also known as an Abstract Syntax Tree (AST).

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