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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Datomic vs HarperDB

Datomic vs HarperDB

OverviewComparisonAlternatives

Overview

Datomic
Datomic
Stacks62
Followers75
Votes0
HarperDB
HarperDB
Stacks6
Followers18
Votes9

Datomic vs HarperDB: What are the differences?

<Write Introduction here>
  1. Data Model: Datomic is based on the concept of immutable, append-only facts, while HarperDB uses a traditional table-based relational database model.

  2. Query Language: Datomic utilizes Datalog, a logic programming language, for querying data, whereas HarperDB supports SQL for querying databases.

  3. Storage Architecture: Datomic operates on an append-only storage architecture while HarperDB uses a combination of in-memory storage and disk persistence.

  4. Deployment Options: Datomic offers cloud-based deployment options such as AWS, Azure, and GCP, whereas HarperDB is designed for on-premise deployment but can also be deployed in the cloud.

  5. Native ACID Transactions: Datomic provides native support for ACID transactions across data distributed in multiple databases, while HarperDB also supports ACID compliance but within a single database instance.

  6. Scalability: Datomic is optimized for immutable data sets and is well-suited for write-once, read-many use cases, while HarperDB is designed to handle high-speed reads and writes essential for real-time applications.

In Summary, Datomic and HarperDB differ in their data model, query language, storage architecture, deployment options, support for native ACID transactions, and scalability.

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Detailed Comparison

Datomic
Datomic
HarperDB
HarperDB

Build flexible, distributed systems that can leverage the entire history of your critical data, not just the most current state. Build them on your existing infrastructure or jump straight to the cloud.

Harper takes the "stack" out of "tech stack" by combining data storage, caching, application, and messaging functions into a single technology to achieve unmatched global low latency, simplicity, and cost performance at scale.

-
Cloud; Edge Computing; On Prem; Globally Distributed; Custom Functions; Database-as-a-service; Hybrid Cloud; Clustering and Replication; Fully-Indexed; Dynamic Schema; Small Footprint; SQL Query Engine; Full NoSQL Capabilities; Configurable Table-Level Pub/Sub; Built In API with Single End Point; Role Based Security; User Friendly Management Studio; Industry Standard Interfaces & Drivers;
Statistics
Stacks
62
Stacks
6
Followers
75
Followers
18
Votes
0
Votes
9
Pros & Cons
No community feedback yet
Pros
  • 2
    Data api
  • 1
    Flexibility
  • 1
    Distribution capabilities
  • 1
    Cost efficient
  • 1
    Performance
Integrations
No integrations available
Node.js
Node.js
GraphQL
GraphQL
Docker
Docker
.NET
.NET
Kubernetes
Kubernetes
Amazon S3
Amazon S3
React.js Boilerplate
React.js Boilerplate
uWebSockets
uWebSockets
Python
Python
Rust
Rust

What are some alternatives to Datomic, HarperDB?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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