YugaByte DB

The YugaByte Database Blog

Thoughts on open source, cloud native and distributed databases

Distributed SQL How It Works How To MongoDB PostgreSQL
April 3, 2019
by Sid Choudhury

How to Achieve High Availability, Low Latency & GDPR Compliance in a Distributed SQL Database

Today’s developers understand that the key requirement to converting and retaining customers is all about delivering fast and responsive experiences, while remaining resilient to failures and compliant with data governance regulations. YugaByte DB is purpose built for geo-distributed applications that require high availability, high performance and regulatory compliance. In this blog, we are going to “look under the hood,” to explore exactly how YugaByte DB distributes data across multiple clouds,

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ACID Transactions Distributed SQL How It Works MongoDB PostgreSQL
March 5, 2019
by Sid Choudhury

YugaByte DB High Availability & Transactions for PostgreSQL & MongoDB Developers

In the first post of our series comparing YugaByte DB with PostgreSQL and MongoDB, we mapped the core concepts in YugaByte DB to the two popular databases. This post is a deeper dive into the high availability and transactions architecture of these databases.

High Availability

Almost all databases including YugaByte DB use replication to ensure that the database remains highly available under failures.

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Distributed SQL How It Works MongoDB PostgreSQL
February 28, 2019
by Sid Choudhury

Mapping YugaByte DB Concepts to PostgreSQL and MongoDB

If you are developing a new distributed application or are extending an existing one with a new set of microservices, chances are you are going to need to store data in a distributed SQL database. The plethora of niche databases that have emerged over the last decade make the task of selecting a database challenging. With many databases, each with its own nomenclature and nuances to choose from,

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ACID Transactions Amazon DynamoDB Microsoft Azure Cosmos DB MongoDB
December 17, 2018
by Sid Choudhury

Why are NoSQL Databases Becoming Transactional?

The NoSQL database revolution started with the publication of the Google BigTable and Amazon Dynamo papers in 2006 and 2007 respectively. These original designs focused on horizontal write scalability without compromising the performance observed in the single node databases dominant at that time. The compromises instead came either in the form of eventual consistency (i.e. inability to read the last update) or loss of multi-key access patterns (such as SQL integrity/foreign key constraints,

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Apache Cassandra Databases How It Works MongoDB PostgreSQL
November 21, 2018
by Sid Choudhury

Data Modeling Basics – PostgreSQL vs. Cassandra vs. MongoDB

Application developers usually spend considerable time evaluating multiple operational databases to find that one database that’s best fit for their workload needs. These needs include simplified data modeling, transactional guarantees, read/write performance, horizontal scaling and fault tolerance. Traditionally, this selection starts out with the SQL vs. NoSQL database categories because each category presents a clear set of trade-offs. High performance in terms of low latency and high throughput is usually treated as a non-compromisable requirement and hence is expected in any database chosen.

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ACID Transactions MongoDB YugaByte DB 1.1
November 1, 2018
by Sid Choudhury

Are MongoDB’s ACID Transactions Ready for High Performance Applications?

Web app developers initially adopted MongoDB for its ability to model data as “schemaless” JSON documents. This was a welcome relief to many who were previously bitten by the rigid structure and schema constraints of relational databases. However, two critical concerns that have been a thorn on MongoDB’s side over the years are that of data durability and ACID transactions.

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Apache Cassandra Community News How It Works MongoDB YugaByte DB 1.1
September 26, 2018
by Karthik Ranganathan

YugaByte DB 1.1 New Feature: Document Data Modeling with the JSON Data Type

Welcome to another post in our ongoing series that highlights new features from the latest 1.1 release announced last week. Today we are going to look at document data modeling using the native JSON data type available in YugaByte DB’s Cassandra compatible YCQL API. Note that this data type is specific to YugaByte DB and is not part of the standard Cassandra Query Language (CQL).

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Apache Cassandra Community News How It Works MongoDB YugaByte DB 1.1
September 25, 2018
by Karthik Ranganathan

YugaByte DB 1.1 New Feature: Speeding Up Queries with Secondary Indexes

Welcome to another post from our ongoing series where we highlight a new feature from the latest 1.1 release! Today we are going to look at secondary indexes.

Defining Secondary Indexes

A database index is a data structure that improves the speed of data retrieval operations on a database table. Typically, databases are very efficient at looking up data by the primary key.

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DynamoDB vs MongoDB vs Cassandra for Fast Growing Geo-Distributed Apps

Amazon DynamoDB is a popular NoSQL database choice for mid-to-large enterprises. In this post, we look beyond Amazon’s marketing claims to explore how well DynamoDB satisfies the core technical requirements of fast growing geo-distributed apps with low latency reads, a common use case found in today’s enterprises. We examine the development, operational and financial consequences of working around the limitations of DynamoDB when attempting to “force-fit” for this use case.

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Amazon DynamoDB Apache Cassandra MongoDB
July 10, 2018
by Karthik Ranganathan
How It Works How To MongoDB
March 7, 2018
by Sid Choudhury

Overcoming MongoDB Sharding and Replication Limitations with YugaByte DB

A few of our early users have chosen to build their new cloud applications on YugaByte DB even though their current primary datastore is MongoDB. Starting with the v3.4 release in Nov 2016, MongoDB has made improvements in its sharding and replication architecture that has allowed it to be re-classified as a Consistent and Partition-tolerant (CP) database and move away from its Available and Partition-tolerant (AP) origins.

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