In-Memory Databases That Work Great With Python

In recent years, the demand for high-performance and scalable data storage solutions has skyrocketed with the exponential growth of data. Traditional database solutions are no longer sufficient to meet the increasing demands of modern applications. To address this challenge, in-memory databases have emerged as a powerful alternative that can process and analyze vast amounts of data in real time. And if you're a Python developer looking for an efficient way to work with in-memory databases, you're in luck. In this blog post, we'll explore some of the top in-memory databases that work seamlessly with Python.

What is an in-memory database?

An in-memory database is a database that is kept in the main memory (RAM) of a computer and controlled by an in-memory database management system. When analyzing information in an in-memory database, only the RAM is used.

Compared to standard disk-based databases, an in-memory database enables quicker reads and writes. In traditional databases, each activity demands a write/read from the disc, an input/output (IO) activity. This added step slows the disk-based systems.

While in-memory databases don't go to the storage device and look through the entire stack when you like to read or write records, you can instantaneously access data stored in the memory.

Why use an in-memory database?

Millions of gadgets, such as security and safety devices, generate data every minute. Real-time analysis of this data is critical. To analyze real-time data, businesses require high-performance database solutions. In-memory databases assist companies in increasing productivity by accelerating database processes. It also enables them to make use of the advantages of big data.

Integrating in-memory databases with programming language improves efficiency and effectiveness for developers. It allows developers to work on data in memory, commit modifications to memory, proactively cache data, revert changes as needed, and dynamically modify definitions.

Top 5 in-memory database solutions

Python also supports various in-memory databases that work great. Let's check out the top five popular in-memory databases.

1. Redis

Redis is a distributed cache and an in-memory database. It has several functional data structures that enable lightning-fast access to data. Keys and values make up the Redis data structure. Instead of keeping data in tables and rows, Redis stores a group of identifiers (keys) and the data in a data dictionary (values). Redis also includes many data types, such as binary-safe texts, groups, collections, and hashing.

Redis also supports different programming languages. When combined with a language like Python, Redis can execute fast and memory-efficient computations with minimum configuration. A Python-Redis client is required to connect to and use Redis with Python. We will use Redis-py for this procedure because it is simple to use and configure.

Pros

• Redis isn't just another caching solution; it includes complex data structures that allow it to store data and retrieve information in ways that a standard key cache can't.
• Redis is easy to install, utilize, and understand.
• Redis operates significantly better for reading and writing as workloads rise for all types of workloads.

Cons

• For non-trivial data sources, Redis requires significantly more RAM to save the same volume of data.
• At the instance level, Redis only provides basic safety (in terms of access privileges). All RDBMS offer fine-grained access control sets per object.
• Redis has a restriction on the size of values. It would be best always to consider your Redis K, V size when utilizing Redis, particularly in Redis clusters.

2. SQLite

SQLite is a C module that supports a disk-based database that doesn't need a dedicated server program and may be accessed using a nonstandard SQL query language. SQLite can be used for data storage within several programs.

It's also feasible to use SQLite to design an application before moving the code to a more robust database like PostgreSQL or Oracle. SQLite in-memory databases are stored in memory rather than on a disc.

The Python SQLite3 module connects Python to the SQLite database. It is a standardized Python DBI API 2.0 that provides an easy interface for communicating with SQLite databases. This module is included with Python after version 2.5x. Therefore, there is no requirement to install it individually.

Pros

• All 32-bit and 64-bit operating systems, as well as big- and little-endian architectures, are supported.
• Allows you to work on many databases at the same period in the same session.
• It organizes the information in table form with a file size of less than 1 MB for the entire database. It allows us to save a significant amount of physical space.

Cons

• Due to file system restrictions, SQLite can create performance concerns with big datasets.
• SQLite has issues with serialized write operations. For systems that demand concurrency, this might be a severe impediment.
• It doesn't support user management.

3. Memgraph

Memgraph is an in-memory database that supports real-time operational graph apps. Your streaming network connects directly to Memgraph. Data can be ingested from various sources, including Kafka, SQL, and plain CSV files.

Memgraph offers a standardized API for querying your data with Cypher, a popular declarative query language that is simple to design, comprehend, and tune for efficiency.

The property graph data model accomplishes this by storing data in terms of entities, properties, and connections. It is a simple and practical solution to model many real issues without depending on sophisticated SQL schemas.

Memgraph uses Cypher and is tightly integrated with the Python ecosystem, making creating and distributing graph-based apps simple. Pymgclient is a Python-based Memgraph database adaptor. It is only compatible with Python 3.0.

Pros

• It provides advanced algorithms for accurate and fast path computation for applications like navigation systems, delivery systems, and traffic network routing.
• You can visualize and engage with your graph information in actual time to make better decisions regarding your graph app development.
• For indexing, Memgraph employs a strongly concurrent skip queue.

Cons

• It's an in-memory database for graph applications, so—noSQL.

4. Aerospike

Aerospike is a high-performance NoSQL in-memory database that can handle large amounts of data. Aerospike is capable of supporting mission-critical applications with real-time transactional demands. These tasks are responsible for delivering educated and timely judgments in industries such as financial institutions, AdTech, and e-commerce.

The in-memory speed of Aerospike makes application development easier. You'll require an in-memory database with technical expertise if you're using NoSQL rather than MySQL for speed and scalability.

Aerospike also supports the integration of Python. You can use the Aerospike client to create a Python application that uses an Aerospike cluster as its database. The client is responsible for maintaining the cluster's connectivity.

Pros

• Can quickly build a fresh cluster or add to an existing cluster.
• Minimal hardware resource usage, particularly RAM.

Cons

• Load balancing on individual network sections.
• It's not easy to use cross-data center duplication. False data can occur in cross-data center replication.

5. Hazelcast

Hazelcast IMDG (In-Memory Data Grid) is an open-source shared in-memory database built on Java. It distributes and replicates data over a cluster of servers, enabling full functionality, flexibility, and easy horizontal scaling.

A typical database saves data on a server's hard discs and then puts them into memory for analysis when it's time to handle them. The Hazelcast Platform retains data in memory from the start, eliminating the need to fetch them from disk and speeding up processing. Save your data in RAM, distribute and replicate it over a cluster of devices, and compute it locally.

Hazelcast has APIs for several popular languages, including Python. The Hazelcast Python client allows you to interface with and utilize Hazelcast clusters. The client offers an asynchronous future-based API that can be used in various scenarios.

Pros

• Hazelcast provides high-speed data read/write accessibility since all data is stored in memory.
• Hazelcast allows data to be distributed between machines and includes backup functionality. It shows that the data is not kept on a single computer. As a result, even if a device fails, joined in a distributed system, the information is not damaged.
• Load balancing.

Cons

• Hazelcast does not have fully managed support (Azure Cache, IBM Bluemix).
• Hazelcast multi-threaded model cannot protect against the split-brain problem during write operations.

Concluding thoughts

Python developers have several options when it comes to working with in-memory databases. Redis, SQLite, Memgraph, Aerospike, and Hazelcast are just a few examples of in-memory databases that work great with Python. Each of these databases has its pros and cons, but they all provide performance and potential for scalability. By utilizing in-memory databases, businesses can increase productivity and take advantage of big data. What is your experience with in-memory databases?