Download E-books Learning Apache Cassandra - Manage Fault Tolerant and Scalable Real-Time Data PDF
- Learn the best way to set up and use Cassandra from the floor up
- Design wealthy schemas that catch the relationships among assorted info varieties utilizing compound fundamental keys
- Master the complicated positive aspects to be had in Cassandra 2.0 via a step by step educational that builds a real-world application's database layer.
Cassandra is a dispensed database that sticks out for its strong function set and intuitive interface, whereas nonetheless offering the excessive availability and scalability of a dispensed store.
Starting from fitting Cassandra and developing your first keyspace, to learning different desk buildings Cassandra deals and exploring the newest and strongest positive aspects of the Cassandra question Language, CQL3, this booklet explores each one subject throughout the lens of a real-world instance software. With lots of examples, advice, and transparent reasons, you are going to grasp compound basic keys, assortment columns, light-weight transactions, and plenty of different complicated features of Cassandra.
By the tip of the e-book, you may be absolutely built to construct strong, scalable Cassandra database layers to your applications.
What you are going to learn
- Install Cassandra and create your first keyspace
- Choose the suitable desk constitution for the duty to hand in quite a few scenarios
- Use variety slice queries for effective info access
- Effortlessly deal with concurrent updates with assortment columns
- Ensure info integrity with light-weight transactions and logged batches
- Understand eventual consistency and use the best consistency point in your situation
- Implement top practices for information modeling and access
About the Author
Mat Brown is a certified software program engineer in Brooklyn, manhattan. In his profession, he has excited by development consumer-facing internet and cellular functions for a number of businesses; he at the moment works at Genius.
Table of Contents
- Getting up and working with Cassandra
- The First Table
- Organizing similar Data
- Beyond Key-value Lookup
- Establishing Relationships
- Denormalizing information for optimum Performance
- Expanding Your information Model
- Collections, Tuples, and User-defined Types
- Aggregating Time-series Data
- How Cassandra Distributes Data
Read or Download Learning Apache Cassandra - Manage Fault Tolerant and Scalable Real-Time Data PDF
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Additional info for Learning Apache Cassandra - Manage Fault Tolerant and Scalable Real-Time Data
Figuring out that we've got reached the top of bob's partition, let's examine if we will be able to locate extra ends up in walls which are located after bob's: decide upon * FROM "user_status_updates" the place TOKEN("username") > TOKEN('bob') restrict 2; by way of inquiring for usernames whose partition token is bigger than bob's token, we make sure that we come again effects we have not noticeable but. The rows back are the 1st in alice's prestige updates: for the reason that we asked rows and retrieved rows, we all know that there will be extra rows in alice's partition. once more, we ask for identification values more than the final one who we observed in alice's prestige updates: choose * FROM "user_status_updates" the place "username" = 'alice' AND identification > 3f9b5f00-e8f7-11e3-9211-5f98e903bf02 restrict three; The question returns final prestige updates for alice: due to the fact that we requested for 3 rows and purely acquired again , we all know that we have now now visible all of the prestige updates in alice's partition. Now, we must always money if there are to any extent further walls to iterate over; we are going to simply ask for one, because we have already got the 1st rows of this web page: pick out * FROM "user_status_updates" the place TOKEN("username") > TOKEN('alice') restrict 1; after we run this question, we will locate that no rows are lower back. This tells us that we now have reached the final web page. this type of two-step pagination may appear a section hard, however the technique will be expressed concisely. We those steps until eventually not more rows have been back: Ask for the 1st rows within the desk. Ask for the following rows within the related partition because the final row that used to be lower back. Repeat until eventually not more rows are back for this partition. Ask for rows within the subsequent partition. return to step 2. construction an autocomplete functionality to date, we have now been serious about storing clients and their prestige updates, yet we will use our wisdom of compound basic keys to make it a piece more uncomplicated to write down prestige updates too. Let's introduce a hashtagging functionality into the prestige replace composition interface, after which autocomplete hashtags as clients variety them. First, we will arrange a desk to shop hashtags utilizing the subsequent question: CREATE desk "hash_tags" ( "prefix" textual content, "remaining" textual content, "tag" textual content, basic KEY ("prefix", "remaining") ); The constitution of our desk is a piece strange however it will paintings rather well for our reasons. The partition secret is prefix, which we will use to shop the 1st letters of every hashtag. The clustering column, last, will shop the remainder letters of the hashtag, and tag will comprise the full hashtag begin to end. by way of partitioning the desk this manner, we will make issues effortless for Cassandra by way of instantly narrowing down the record of attainable autocomplete tags to these within the partition, pointed out by means of the two-letter prefix. This does, after all, suggest that we won't autocomplete entries of fewer than letters, yet it is a lovely general obstacle. to work out how the autocomplete works, let's upload a few seed info into the desk: INSERT INTO "hash_tags" ("prefix", "remaining", "tag") VALUES ('ca', 'ssandra', 'cassandra'); INSERT INTO "hash_tags" ("prefix", "remaining", "tag") VALUES ('ca', 'ssette', 'cassette'); INSERT INTO "hash_tags" ("prefix", "remaining", "tag") VALUES ('ca', 'sual', 'casual'); INSERT INTO "hash_tags" ("prefix", "remaining", "tag") VALUES ('ca', 'ke', 'cake'); Now, for instance the person starts off typing a hashtag, and has thus far typed ca.