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InfluxDB vs RocksDB: What are the differences?
InfluxDB and RocksDB are two popular databases with distinct features. InfluxDB is a time-series database optimized for high write and query performance, primarily used for monitoring and IoT applications. On the other hand, RocksDB is a high-performance embedded key-value store designed for SSDs and flash memory.
Data Model: InfluxDB uses a schema-less data model with measurement, tag keys, tag values, and fields, allowing for flexible data organization and querying. In contrast, RocksDB employs a simple key-value data model, offering high efficiency and low latency for key-based operations.
Storage Engine: InfluxDB utilizes the Time-Structured Merge (TSM) Tree storage engine, optimized for time-series data storage and retrieval. RocksDB, on the other hand, employs a persistent log-structured merge-based storage engine, providing efficient write and read performance for key-value data.
Query Language: InfluxDB supports a SQL-like query language called InfluxQL, designed specifically for time-series data manipulation and analysis. RocksDB does not provide a query language built-in, requiring developers to implement custom query logic for data retrieval and processing.
Use Cases: InfluxDB is commonly used in applications requiring real-time analytics, monitoring, and observability, where time-series data plays a crucial role. RocksDB is preferred for embedded applications, cache storage, and key-value scenarios where high performance and low latency are essential.
Scalability: InfluxDB offers horizontal scalability through clustering and sharding mechanisms, enabling seamless distribution of data across multiple nodes for high availability and performance. RocksDB is typically deployed in single-node configurations, limiting scalability options to vertical scaling by enhancing hardware resources.
Community and Ecosystem: InfluxDB has a thriving open-source community and a rich ecosystem of integrations, plugins, and tools tailored for time-series data management. RocksDB, maintained by Facebook, has a strong developer community for key-value store use cases, with contributions and support from various organizations.
In Summary, InfluxDB excels in time-series data management and real-time analytics, while RocksDB is optimized for high-performance key-value storage and retrieval, each tailored for specific use cases and scenarios.
I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!
You've probably come to a decision already but for those reading...here are some resources we put together to help people learn more about Milvus and other databases https://zilliz.com/comparison and https://github.com/zilliztech/VectorDBBench. I don't think they include RocksDB or HBase yet (you could could recommend on GitHub) but hopefully they help answer your Elastic Search questions.
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 InfluxDB
- Time-series data analysis58
- 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 InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
Cons of RocksDB
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