5 posts tagged with "database"

Today, we're excited to share that Tigris Search has moved into beta.

Over the coming days, we'll share more about the features available in Tigris Search. But in this post, we'd like to focus on a feature that feels magical and we believe differentiates Tigris from the competition: Tigris Database to Search automatic synchronization.

Tigris Database to Search automatic synchronization allows you to automatically create search indexes and synchronize your data from Tigris Database to Tigris Search. You don't need to spin up any new and costly infrastructure or add any complex configuration to take advantage of this. All you need to do is update your Tigris data model definitions!

Tigris is an open source developer data platform that makes building data-rich serverless applications a breeze. It enables developers to stick to just being developers and not be forced into DevOps.

Tigris uses FoundationDB's transactional key-value interface as its underlying storage engine. In our blog post Skipping the boring parts of building a database using FoundationDB we went into the details of why we chose to build on FoundationDB. To recap, FoundationDB is an ordered, transactional, key-value store with native support for multi-key strictly serializable transactions across its entire keyspace. We leverage FoundationDB to handle the hard problems of durability, replication, sharding, transaction isolation, and load balancing so we can focus on higher-level concerns.

We are starting a series of blog posts that go into the details of how Tigris has been implemented. In the first post of the series, we will share the details of how we have built the multi-model document layer on top of FoundationDB. We will cover the topics of data layout, and schema management.

The most complicated and time-consuming parts of building a new database system are usually the edge cases and low-level details. Concurrency control, consistency, handling faults, load balancing, that kind of thing. Almost every mature storage system will have to grapple with all of these problems at one point or another. For example, at a high level, load balancing hot partitions across brokers in Kafka is not that different from load balancing hot shards in MongoDB, but each system ends up re-implementing a custom load-balancing solution instead of focusing on their differentiating value to end-developers.

This is one of the most confusing aspects of the modern data infrastructure industry, why does every new system have to completely rebuild (not even reinvent!) the wheel? Most of them decide to reimplement common processes and components without substantially increasing the value gained from reimplementing them. For instance, many database builders start from scratch when building their own storage and query systems, but often merely emulate existing solutions. These items usually take a massive undertaking just to get basic features working, let alone correct.

In our inaugural blog post Hello world, we talked about the problem of data infrastructure sprawl that, over the years, has complicated modern application development, while putting a lot of burden on the operations team who must manage, maintain and secure many different databases and technologies.

In this blog post, we’ll explain what we mean when we say "data infrastructure sprawl" by walking through a typical example, and then we’ll explain why it doesn’t have to be this way.

We're excited to announce the launch of Tigris Data, a company on the mission of simplifying data management for developers.

Over the years, data has become increasingly complex and difficult to manage. Developers have had their lives made exponentially more difficult due in large part to all these different technologies, data models, APIs, and databases they're expected to put together to build modern applications.

The database sprawl also puts a lot of pressure on operations teams, who must manage, maintain and secure these different databases and technologies. Then there is the onerous task of operationalizing these databases across multiple different cloud platforms.