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InfluxDB vs LevelDB: What are the differences?
InfluxDB and LevelDB are both popular database systems, but they have key differences that set them apart.
Data Model: InfluxDB is a time-series database designed for handling data with timestamps, making it ideal for storing and analyzing time-sensitive data such as monitoring statistics or IoT sensor data. On the other hand, LevelDB is a key-value database that stores data in a simple key-value format without any specific support for time-series data.
Query Language: InfluxDB uses InfluxQL, a SQL-like query language specifically optimized for time-series data queries. This includes functions and clauses tailored for time-series analysis. In contrast, LevelDB does not have a built-in query language and relies on simple key lookup operations for data retrieval.
Data Storage: InfluxDB is optimized for high-volume writes and reads, making it suitable for real-time data processing. It also supports data compression and retention policies for efficient storage management. LevelDB, on the other hand, is optimized for fast key-value lookups and is more suitable for lightweight applications that do not require time-series data handling.
Indexing: InfluxDB automatically indexes timestamp data, which accelerates queries based on time ranges. This indexing scheme is essential for efficiently querying time-series data. LevelDB, while efficient for key lookups, does not provide specialized indexing for time-series data, which can impact the performance of time-sensitive queries.
Scalability: InfluxDB is designed to handle large amounts of time-series data and offers clustering and sharding capabilities for horizontal scalability. This makes it suitable for applications that require scaling out to multiple nodes. LevelDB, while efficient for single-node deployments, may face challenges in scaling horizontally due to limitations in its architecture.
Use Cases: InfluxDB is commonly used in applications that deal with metrics, analytics, and monitoring where time-series data plays a crucial role. On the other hand, LevelDB is suitable for scenarios requiring fast key-value lookups and simple data storage without the need for time-series analysis capabilities.
In Summary, InfluxDB excels in handling time-series data with specialized query language and indexing, while LevelDB is more focused on key-value storage with efficient key lookups but lacks dedicated support for time-series data analysis.
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 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 LevelDB
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Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
Cons of LevelDB
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What is InfluxDB?
What is LevelDB?
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