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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. MongoDB vs Symas LMDB

MongoDB vs Symas LMDB

OverviewDecisionsComparisonAlternatives

Overview

MongoDB
MongoDB
Stacks96.6K
Followers82.0K
Votes4.1K
GitHub Stars27.7K
Forks5.7K
Symas LMDB
Symas LMDB
Stacks17
Followers36
Votes0

MongoDB vs Symas LMDB: What are the differences?

Introduction

MongoDB and Symas LMDB are both databases, but they have several key differences that set them apart from each other.

  1. Structure and Data Model: MongoDB is a document-oriented database, while Symas LMDB is a key-value store. MongoDB stores data in documents, which are similar to JSON objects, while Symas LMDB stores data as key-value pairs. This difference in data model affects how data is organized and accessed in each database.

  2. Scalability and Performance: MongoDB is designed to scale horizontally, meaning it can handle large amounts of data by distributing it across multiple servers. Symas LMDB, on the other hand, is designed for high-performance reads and writes on a single server. This makes Symas LMDB a good choice for applications that require low-latency access to data.

  3. Querying and Indexing: MongoDB provides a powerful querying language that allows for complex queries on documents. It also supports flexible indexing options to optimize query performance. Symas LMDB, being a key-value store, does not provide the same level of query flexibility. It supports simple key-based lookups and does not have built-in indexing capabilities.

  4. ACID Transactions: MongoDB supports multi-document ACID transactions, which means that multiple operations can be grouped together into a single transaction that is atomic, consistent, isolated, and durable. Symas LMDB, on the other hand, does not support ACID transactions. It focuses on maximizing performance by providing low-level data access.

  5. Memory Usage: MongoDB can use a significant amount of memory to store its indexes and cache frequently accessed data. Symas LMDB, on the other hand, uses memory-mapped files and has a more efficient memory management strategy. This makes Symas LMDB consume less memory compared to MongoDB for the same amount of data.

  6. Durability and Persistence: MongoDB provides durability and persistence by writing data to disk using a write-ahead log (WAL). This ensures that data is not lost in the event of a system failure. Symas LMDB, being an in-memory database, does not provide the same level of durability and persistence. It relies on regular disk flushes to maintain data durability.

In summary, MongoDB and Symas LMDB differ in their data model, scalability, querying capabilities, transaction support, memory usage, and durability. While MongoDB is well-suited for large-scale applications that require flexible querying and transactions, Symas LMDB excels in low-latency read and write operations on a single server.

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Advice on MongoDB, Symas LMDB

George
George

Student

Mar 18, 2020

Needs adviceonPostgreSQLPostgreSQLPythonPythonDjangoDjango

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k
Comments
Ido
Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k
Comments
Mike
Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k
Comments

Detailed Comparison

MongoDB
MongoDB
Symas LMDB
Symas LMDB

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.

It is an extraordinarily fast, memory-efficient database which is developed for the OpenLDAP Project. With memory-mapped files, it has the read performance of a pure in-memory database while retaining the persistence of standard disk-based databases.

Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
Ordered-map interface; Fully-transactional; Multi-thread and multi-process concurrency supported
Statistics
GitHub Stars
27.7K
GitHub Stars
-
GitHub Forks
5.7K
GitHub Forks
-
Stacks
96.6K
Stacks
17
Followers
82.0K
Followers
36
Votes
4.1K
Votes
0
Pros & Cons
Pros
  • 829
    Document-oriented storage
  • 594
    No sql
  • 554
    Ease of use
  • 465
    Fast
  • 410
    High performance
Cons
  • 6
    Very slowly for connected models that require joins
  • 3
    Not acid compliant
  • 2
    Proprietary query language
No community feedback yet
Integrations
No integrations available
Python
Python
Linux
Linux
Java
Java
Windows
Windows
macOS
macOS

What are some alternatives to MongoDB, Symas LMDB?

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.

InfluxDB

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

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