Learning Apache Cassandra - Manage Fault Tolerant and Scalable Real-Time Data

Key Features

  • Learn how one can set up and use Cassandra from the floor up
  • Design wealthy schemas that seize the relationships among diversified facts varieties utilizing compound basic keys
  • Master the complex beneficial properties on hand in Cassandra 2.0 via a step by step instructional that builds a real-world application's database layer.

Book Description

Cassandra is a disbursed database that stands proud for its powerful function set and intuitive interface, whereas nonetheless supplying the excessive availability and scalability of a dispensed store.

Starting from fitting Cassandra and developing your first keyspace, to learning the several desk constructions Cassandra bargains and exploring the newest and strongest good points of the Cassandra question Language, CQL3, this publication explores each one subject during the lens of a real-world instance program. With lots of examples, counsel, and transparent causes, you will grasp compound basic keys, assortment columns, light-weight transactions, and plenty of different complex elements of Cassandra.

By the top of the ebook, you will be totally outfitted to construct strong, scalable Cassandra database layers to your applications.

What you are going to learn

  • Install Cassandra and create your first keyspace
  • Choose definitely the right desk constitution for the duty handy in numerous scenarios
  • Use variety slice queries for effective information access
  • Effortlessly deal with concurrent updates with assortment columns
  • Ensure information integrity with light-weight transactions and logged batches
  • Understand eventual consistency and use the best consistency point to your situation
  • Implement most sensible practices for information modeling and access

About the Author

Mat Brown is a qualified software program engineer in Brooklyn, big apple. In his occupation, he has considering construction consumer-facing internet and cellular purposes for numerous businesses; he at present works at Genius.

Table of Contents

  1. Getting up and operating with Cassandra
  2. The First Table
  3. Organizing similar Data
  4. Beyond Key-value Lookup
  5. Establishing Relationships
  6. Denormalizing facts for max Performance
  7. Expanding Your info Model
  8. Collections, Tuples, and User-defined Types
  9. Aggregating Time-series Data
  10. How Cassandra Distributes Data

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The worth of the telephone is, straightforwardly, the worth of the physique column within the row with the status_update_id worth 16e2f240-2afa-11e4-8069-5f98e903bf02. the explanation we in simple terms see one RowKey worth is simply because the entire rows within the home_status_updates desk have the partition key alice. What cassandra-cli calls a RowKey is, in truth, precisely what now we have been calling a partition key the complete time. a whole mapping With this perception, we will be able to now generate a whole mapping among the parts of a column kinfolk and the CQL-level desk constitution elements they characterize: Column kinfolk part CQL3 desk part RowKey Partition Key mobile identify The values of clustering column(s), if any, through the identify of the knowledge column telephone worth the price within the row/column pointed out via the partition key, clustering column(s), and the column identify to augment this concept, let's observe our visualization from the former part to the extra advanced constitution of home_status_updates: the most perception this is that each price in each row in alice's partition is kept in a one-dimensional facts constitution within the column kinfolk. on the column kinfolk point, not anything within the garage constitution distinguishes the values in carol's prestige replace row from the values in dave's prestige replace row. The cells containing these values easily have various prefixes of their phone names; purely on the CQL point are those represented as targeted rows. The extensive row information constitution one other very important statement is that, on the column kinfolk point, there isn't any preset checklist of allowed telephone names. The mobilephone names we notice in alice's partition of the home_status_updates column relatives are derived in part from facts calculated at runtime—namely, the UUID values of varied prestige replace identity columns. on the CQL point, tables should have predefined column names yet no similar restrict exists on the column family members point. having a look heavily on the cells in alice's RowKey, we notice that they're so as: first, descending through the timestamp encoded within the UUID portion of the phone identify, and moment, alphabetically ascending through the column identify part. You discovered in bankruptcy three, Organizing comparable information that clustering columns supply CQL rows a common ordering, and now we all know how the cells grouped lower than a given RowKey on the column relations point are constantly ordered via the telephone identify. the information constitution that we discover at alice's RowKey in home_status_updates is often known as a large row. to not be burdened with the rows we in general have interaction with in CQL tables, extensive rows are easily ordered collections of cells; every one huge row is linked to a RowKey, which on the CQL point we name a partition key. The empty mobilephone now we have to this point missed one curious entity within the column family members info structure—each RowKey turns out to have a mobilephone whose price is empty, and whose identify is composed simply of a clustering column price and a delimiter. bear in mind that, in a CQL row, there's no requirement that any info column include info.

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