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HBase vs InfluxDB vs RocksDB: What are the differences?
Introduction
When comparing HBase, InfluxDB, and RocksDB, it is essential to understand the key differences among these databases. Each database has its unique features and use cases, making it crucial to choose the right one based on specific requirements. Below are the key differences between HBase, InfluxDB, and RocksDB.
Data Model: HBase is a column-oriented database that stores data in columns rather than rows, offering fast read and write operations for large datasets. InfluxDB, on the other hand, is specifically designed for time-series data, making it efficient for storing and retrieving time-stamped data points. RocksDB is a key-value store that provides high performance for read-heavy workloads, making it suitable for applications requiring fast access to individual records.
Use Cases: HBase is commonly used in applications that require real-time read and write capabilities, such as social media platforms and e-commerce websites. InfluxDB is ideal for applications that deal with monitoring, IoT sensor data, and DevOps metrics due to its efficient storage and retrieval of time-series data. RocksDB is often used in scenarios that demand low latency and high throughput, like caching layers and logging systems.
Query Language: HBase uses Apache HBase API or Apache Phoenix SQL for querying data, allowing users to perform complex operations on large datasets. InfluxDB uses its query language, InfluxQL, which is specifically optimized for time-series data manipulation, making it easier to work with timestamped datasets. RocksDB does not have a query language built-in and is typically used as an embedded database supporting key-value operations.
Consistency Model: HBase provides strong consistency guarantees, ensuring that data is always up to date and accurate across multiple nodes in a distributed environment. InfluxDB offers eventual consistency, prioritizing availability and partition tolerance over strict consistency, which may lead to stale data under certain conditions. RocksDB provides strong consistency at the single-node level but may exhibit eventual consistency in distributed configurations.
Scalability: HBase is horizontally scalable, allowing users to add more nodes to the cluster for increased storage capacity and performance. InfluxDB also supports horizontal scalability, enabling users to scale out by adding more nodes as data volume grows. RocksDB is more suited for single-node deployments or limited-scale distributed settings, making it less suitable for massive scalability requirements.
In Summary, understanding the key differences between HBase, InfluxDB, and RocksDB in terms of data model, use cases, query language, consistency model, and scalability can help in making informed decisions when choosing the appropriate database for specific applications.
I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.
Druid Could be an amazing solution for your use case, My understanding, and the assumption is you are looking to export your data from MariaDB for Analytical workload. It can be used for time series database as well as a data warehouse and can be scaled horizontally once your data increases. It's pretty easy to set up on any environment (Cloud, Kubernetes, or Self-hosted nix system). Some important features which make it a perfect solution for your use case. 1. It can do streaming ingestion (Kafka, Kinesis) as well as batch ingestion (Files from Local & Cloud Storage or Databases like MySQL, Postgres). In your case MariaDB (which has the same drivers to MySQL) 2. Columnar Database, So you can query just the fields which are required, and that runs your query faster automatically. 3. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. 4. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures 5. Gives ana amazing centralized UI to manage data sources, query, tasks.
We are building an IOT service with heavy write throughput and fewer reads (we need downsampling records). We prefer to have good reliability when comes to data and prefer to have data retention based on policies.
So, we are looking for what is the best underlying DB for ingesting a lot of data and do queries easily
We had a similar challenge. We started with DynamoDB, Timescale, and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us a We had a similar challenge. We started with DynamoDB, Timescale and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us better performance by far.
Druid is amazing for this use case and is a cloud-native solution that can be deployed on any cloud infrastructure or on Kubernetes. - Easy to scale horizontally - Column Oriented Database - SQL to query data - Streaming and Batch Ingestion - Native search indexes It has feature to work as TimeSeriesDB, Datawarehouse, and has Time-optimized partitioning.
if you want to find a serverless solution with capability of a lot of storage and SQL kind of capability then google bigquery is the best solution for that.
I chose TimescaleDB because to be the backend system of our production monitoring system. We needed to be able to keep track of multiple high cardinality dimensions.
The drawbacks of this decision are our monitoring system is a bit more ad hoc than it used to (New Relic Insights)
We are combining this with Grafana for display and Telegraf for data collection
Pros of HBase
- Performance9
- OLTP5
- Fast Point Queries1
Pros of InfluxDB
- Time-series data analysis59
- Easy setup, no dependencies30
- Fast, scalable & open source24
- Open source21
- Real-time analytics20
- Continuous Query support6
- Easy Query Language5
- HTTP API4
- Out-of-the-box, automatic Retention Policy4
- Offers Enterprise version1
- Free Open Source version1
Pros of RocksDB
- Very fast5
- Made by Facebook3
- Consistent performance2
- Ability to add logic to the database layer where needed1
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Cons of HBase
Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
Cons of RocksDB
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