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Introduction to nosql | NoSQL databases
- 2. INTRODUCTION TO NoSQL•NoSQL, known as Not only SQL database, provides a mechanism for storage and retrieval of data •NoSQL databases are used in real-time web applications and big data •Most of the NoSQL are open source and it has a capability of horizontal scalability which means that commodity kind of machines could be added •It is schema free and there is no requirement to design the tables and pushing the data to it
- 3. AGGREGATE DATA MODELS•Aggregate is a term that comes from DDD(Domain-Driven Design) •In DDD, an aggregate is a collection of data that we interact with as a unit. •Aggregates make it easier for the database to manage data storage over clusters. •4 aggregate data models – Key-value, Document, Graph and Column-family
- 4. KEY-VALUE DATA MODEL•The aggregate is opaque-that is we can store whatever we like in the aggregate •We can only access an aggregate by lookup based on its key •The key-value database is a very DOCUMENT DATA MODEL•It is able to see a structure in the aggregate but imposes limits on what we can place in it. We get more flexibility when accessing data •We can submit queries to the database based on the fields in the aggregate and retrieve part of the
- 5. COLUMN- FAMILY STORES•These are created to store and process very large amounts of data distributed over many machines •Column-family stores are modeled on Google’s Big Table •The first key is often described as a Row Identifier
- 6. GRAPH DATABASE•A graph database is a big dense network structure •It uses sophisticated shortest path algorithms to make data queries more efficient •Graph databases take document databases to the extreme by introducing the concept of type relationships between documents or nodes. The most common example is the relationship between people on a social network such as
- 7. NoSQL databases are Schemaless : •A key-value store allows you to store any data you like under a key •A document database effectively does the same thing , since it makes no restrictions on the structure of the documents you store •Column-family database allow you to store any data
- 8. SCHEMALESS DATABASE•Easily store whatever you need and add new things as you discover them •Makes it easier to deal with nonuniform data: data where each record has a different set of fields •Having the implicit schema in the application means that in order to understand data, you have to dig into the
- 9. MATERIALIZED VIEW•NoSQL databases don’t have views as relational databases, they may have precomputed and cached queries – Materialized Views •2 strategies to manage materialized views oUpdate the materialized view at the same time you update the base data for it oRun batch jobs to update the materialized views at regular intervals
- 10. DISTRIBUTION DATA MODELS•The primary driver of interest in NoSQL has been its ability to run databases on a large cluster •Aggregate orientation fits well with scaling out because the aggregate is a natural unit to use for distribution •Various models for data distribution : •Sharding •Master-slave replication
- 11. SHARDING•Sharding is the technique of putting different parts of the data onto different servers •It is valuable for performance because it can improve both read and write performances •2 main issues in sharding : •How to clump the data, so that one user mostly gets her data from a single server •How to arrange single data clumps on the nodes to provide the best data access
- 12. MASTER-SLAVE REPLICATION•It is most helpful for scaling when you have a read-intensive dataset. •One node is designed as the master and is responsible for processing any updates to that data. •Other nodes are slaves. Replication process synchronizes the slaves with the master.
- 13. PEER-TO-PEER REPLICATION•With a peer-to-peer replication cluster, you can ride over node failures without losing access to data. You can easily add nodes to improve performance. •The biggest complication is consistency. When you write to two different places, you run the risk that two people will attempt to update the same record at the same time: a