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AlaSQL vs InfluxDB: What are the differences?
AlaSQL: JavaScript SQL Database for Browser and Node.js *. Open source SQL database for Javascript with a strong focus on query speed and datasource flexibility for relational data, schemaless data, and graph data. Works in your browser, Node.js and Cordova. Handles both traditional relational tables and nested JSON data (NoSQL). Export, store, and import data from localStorage, IndexedDB, and Excel; *InfluxDB:** An open-source distributed time series database with no external dependencies. InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out..
AlaSQL and InfluxDB can be categorized as "Databases" tools.
Some of the features offered by AlaSQL are:
- Run SQL queries directly on javascript data arrays.
- Bring the comfort of a full database engine to your javascript app.
- Fast SQL based in-memory data processing for BI and ERP applications on fat clients.
On the other hand, InfluxDB provides the following key features:
- Time-Centric Functions
- Scalable Metrics
- Events
AlaSQL and InfluxDB are both open source tools. InfluxDB with 16.7K GitHub stars and 2.39K forks on GitHub appears to be more popular than AlaSQL with 4.51K GitHub stars and 438 GitHub forks.
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 AlaSQL
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
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Cons of AlaSQL
Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
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What is AlaSQL?
What is InfluxDB?
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