MongoDB is a full-fledged database with a more significant memory and CPU footprint, which is not ideal for some narrow use cases like high-frequency time-series data, specifically for storing blobs or large binary objects such as sensor images, telemetry data, and logs. MongoDB design prioritizes flexibility and general purpose rather than specialization and introduces overhead for schema management and query handling. Regarding IoT, ReductStore is a good alternative to MongoDB, as it is built in Rust, a modern language, and focuses on efficient, lightweight blob storage leveraging Rust’s safety and concurrency features to ensure consistent, high-performance operations in use cases like IoT data.

This blog post illustrates MongoDB alternatives and showcases ReductStore as the best alternative for IoT data management. It also highlights key considerations when selecting the best database solution. When it comes to handling IoT data, MongoDB certainly has some limitations. Read on to learn more about MongoDB’s limitations for IoT data management. 

MongoDB’s limitation in Management of the IoT Data

MongoDB is flexible and easy to use, which has made it popular among developers worldwide when it comes to database technology. Due to its flexible, document-oriented model that allows easy storage and retrieval of diverse data types, scalability, performance, and ability to handle large volumes of data efficiently, it is used in many applications. However, several limitations can be seen in the IoT data management. Some of them are like:

1. Degraded High-Frequency Write Operations

MongoDB may show degraded performance when handling high-frequency data writes, a common requirement in IoT environments as its design is focused on read-heavy workloads.

2. Scalability for Time Series Data 

Although MongoDB provides support for time-series collections, it is not made for time-series workloads, which makes it insufficient for the growing data volume. 

3. Storage Overheads

MongoDB’s BSON format can result in higher storage overheads, while specialized time-series databases are lightweight.

The above challenges have made IoT developers and system architects consider alternatives customized to the unique requirements of IoT data management. While searching for an alternative to MongoDB for managing high-frequency IoT data, one option that comes to mind is the ReductStore. Learn more about ReductStore as an alternative to MongoDB below. 

A Specialized Alternative – ReductStore

ReductStore is designed explicitly with optimized features for fast writes, real-time querying, and the facility to store large binary blobs. It captures and stores continuous data streams from IoT devices at high frequencies without performance degradation, while MongoDB might degrade in such environments. 

Explore further below the advantages of ReductStore over MongoDB when managing high-frequency time series unstructured data.   

Advantages of ReductStore Over MongoDB

While MongoDB is a highly popular general-purpose database, some of the advantages of ReductStore over MongoDB regarding IoT data management do exist, which are as follows:

1. Better Performance

Not only do the high-frequency data streams require continuous writes with low latency, but also high throughput. ReductStore stands out as the best option in such use cases. Its Rust-based architecture eliminates runtime overhead, thereby ensuring efficient memory usage. Due to its specialized and optimized design for high-frequency data streams with low latency, ReductStore outperforms MongoDB in the same environment.

2. Scalability for IoT Workloads

MongoDB can scale horizontally very effectively through sharding, allowing it to handle large data volumes. However, there is no denying that coordination and replication processes like shard key management, query routing, balancing data, and write acknowledgment while sharding lead to overhead in use cases requiring high-frequency writes. In contrast, ReductStore’s architecture is customized explicitly for time-series data, ensuring efficient scalability as data volumes grow. ReductStore is made with Rust, which supports safe concurrency and multithreading, which helps it efficiently process parallel data streams from many IoT devices. MongoDB fails to achieve the same level of fine-grained concurrency optimization as Rust-based ReductStore. It is because it relies on traditional multi-threaded paradigms that can introduce overhead in parallel data stream processing.

3. Efficient use of Resources

ReductStore is lightweight due to Rust’s low runtime overhead, which helps it operate efficiently even on resource-constrained devices like IoT edge devices. In contrast, MongoDB requires query parsing, planning and execution, replication, and indexing, which adds overhead. Moreover, various query types require support when we use MongoDB, including complex aggregations and joins, which is resource-intensive, making it less suited for constrained IoT edge devices.  

4. Improved Time-Series Data Handling

ReductStore’s purposely built storage engine is made for time-series data, which can guarantee optimized storage and retrieval abilities customized to high-frequency workloads. MongoDB is not purposefully built to store time series data.

Other Alternatives to Consider

1. InfluxDB:

It is a time series database known for its high performance and intuitive query language.

2. TimescaleDB

TimescaleDB is a PostgreSQL extension optimized for time-series data. It offers SQL reliability and better scalability.

3. Cassandra

Cassandra is a distributed NoSQL database with strong scalability that is best for large-scale IoT deployments.

4. RedisTimeSeries

This Redis module is designed for time series data that can offer fast in-memory performance.

Each of the above has its strengths and trade-offs, requiring evaluation based on the specific requirements of IoT applications.

ReductStore as the Best Alternative

ReductStore’s better performance, scalability, and efficient resource handling for high-frequency IoT unstructured timestamped data make it the best alternative to MongoDB. Moreover, it is purposely built for such use cases. MongoDB and other databases mentioned above are not purposely constructed for such an environment. That makes ReductStore the top choice for IoT-based high-frequency unstructured timestamped data. Aligning the database capabilities with IoT requirements is also essential, as it can help developers and system architects unlock the full potential of IoT systems.

Explore our blog post Alternative to MongoDB for Blob Data to learn more about the comparison between ReductStore and MongoDB.