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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. LevelDB vs OlegDB

LevelDB vs OlegDB

OverviewComparisonAlternatives

Overview

LevelDB
LevelDB
Stacks108
Followers111
Votes0
GitHub Stars38.3K
Forks8.1K
OlegDB
OlegDB
Stacks1
Followers3
Votes0

LevelDB vs OlegDB: What are the differences?

Introduction

LevelDB and OlegDB are both popular embedded key-value storage engines known for their performance and reliability. Understanding the key differences between the two can help in making an informed decision on which one to use for a specific application.

  1. Storage Format: LevelDB uses a log-structured merge-tree (LSM) for storage, while OlegDB utilizes a B-tree structure. This difference in storage format can impact performance and efficiency depending on the type of operations and workload.

  2. Concurrency Control: LevelDB employs a single-threaded design, which can limit its ability to handle high concurrency scenarios efficiently. In contrast, OlegDB is built with multi-threading support, allowing for better scalability and performance in concurrent workloads.

  3. Durability Mechanism: LevelDB relies on write-ahead logging for durability, which can introduce overhead in terms of write amplification. On the other hand, OlegDB offers configurable durability options, such as synchronous writes, to tailor the trade-off between performance and durability according to specific requirements.

  4. Memory Management: OlegDB uses a memory-mapped file approach for managing memory, allowing for direct access to disk-backed data without incurring extra copying. This can result in better performance compared to LevelDB's memory management strategies in certain use cases.

  5. Compaction Strategy: LevelDB performs compaction in the background to merge and compact data files, which can impact performance during these operations. OlegDB, on the other hand, offers more control over compaction strategies, allowing users to optimize performance based on their specific needs.

  6. Indexes Implementation: While LevelDB relies on block-based indexes for data retrieval, OlegDB utilizes a hybrid index structure combining B-trees and LSM-trees for efficient lookup operations. This difference in index implementation can influence the speed and efficiency of data retrieval in both storage engines.

In Summary, understanding the key differences between LevelDB and OlegDB in terms of storage format, concurrency control, durability mechanism, memory management, compaction strategy, and index implementation can help in choosing the right storage engine based on specific performance and scalability requirements.

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Detailed Comparison

LevelDB
LevelDB
OlegDB
OlegDB

It is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values. It has been ported to a variety of Unix-based systems, macOS, Windows, and Android.

It is a concurrent, pretty fast K/V hashtable with an Go frontend. It uses the Murmur3 hashing algorithm to hash and index keys. It operates under a startling new enterprise-ready paradigm which we call MAYO: Marginally available Yolk and Oil database.

Simple key-value stores with Go, C++, Node.js and more!
Keeps track of currently inserted nodes via a splay tree; LZ4 Compression ; Enterprise-ready paradigm
Statistics
GitHub Stars
38.3K
GitHub Stars
-
GitHub Forks
8.1K
GitHub Forks
-
Stacks
108
Stacks
1
Followers
111
Followers
3
Votes
0
Votes
0
Integrations
Java
Java
Windows
Windows
macOS
macOS
Golang
Golang

What are some alternatives to LevelDB, OlegDB?

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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