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HBase vs InfluxDB: What are the differences?
Scalability: HBase is a distributed database system designed to handle massive amounts of data by partitioning data across multiple servers, allowing for horizontal scalability. In contrast, InfluxDB is generally used for time-series data and is optimized for high write throughput and fast query performance for time-stamped data, making it more suitable for time-series data workloads.
Data Model: HBase is a wide-column store that stores data in tables with rows and columns, similar to a relational database, but with the flexibility to have varying columns for each row. InfluxDB, on the other hand, uses a tag-value-based data model where each data point consists of a measurement, tags, fields, and a timestamp, making it optimal for storing time-series data efficiently.
Query Language: HBase uses Apache HBase Shell or Java API for querying and interacting with the data stored in HBase tables. In contrast, InfluxDB uses Influx Query Language (InfluxQL) or Flux (a functional data scripting language) for querying and manipulating time-series data, offering more advanced querying capabilities specifically tailored for time-series data analysis.
Consistency and Availability: HBase offers strong consistency guarantees, ensuring that all reads and writes are immediately consistent across the distributed system. InfluxDB, on the other hand, prioritizes availability and partition tolerance over strict consistency, emphasizing high availability and fault tolerance for time-series data ingestion and query processing.
Use Cases: HBase is commonly used for online transaction processing (OLTP) applications that require random read/write access to large-scale data sets, whereas InfluxDB is preferred for time-series data workloads such as monitoring, IoT sensor data, DevOps metrics, and real-time analytics where high write throughput and fast query performance are crucial.
Ecosystem Integration: HBase integrates well with the Apache Hadoop ecosystem, providing seamless integration with tools like Apache Hive, Apache Spark, and Apache Flume for Big Data processing and analytics, while InfluxDB integrates with various monitoring and visualization tools like Grafana, Prometheus, and Telegraf for real-time monitoring and visualization of time-series data.
In Summary, HBase is more suited for large-scale random read/write applications with strong consistency guarantees, while InfluxDB excels in time-series data workloads with optimized write throughput and query performance.
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 HBase
- Performance9
- OLTP5
- Fast Point Queries1
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