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Druid vs Hadoop: What are the differences?
- Performance and Scalability: Druid is designed to provide real-time query capabilities on large datasets, while Hadoop focuses on distributed storage and batch processing. Druid's architecture allows for efficient ingestion of data and provides fast query responses, making it suitable for interactive analytics. On the other hand, Hadoop's MapReduce framework enables parallel processing of data but may have slower query response times due to its batch processing nature.
- Data Model: Druid stores data in a columnar format optimized for low-latency queries, where each column is stored separately and can be compressed individually. Hadoop, on the other hand, stores data in a distributed file system using the Hadoop Distributed File System (HDFS), where files are stored in a block format. This difference in data storage and compression techniques affects the speed and efficiency of data querying.
- Data Ingestion: Druid is designed to support real-time data ingestion by providing mechanisms to ingest data streams and continuously update its indexes. In contrast, Hadoop is primarily used for batch processing and requires manual loading of data into the HDFS before it can be processed.
- Querying Capabilities: Druid provides sub-second query response times even on large datasets due to its column-based storage and indexing structures. It supports complex aggregations, filtering, and grouping operations, making it suitable for interactive, ad-hoc queries. Hadoop, on the other hand, may have longer query response times as it processes data in batches and requires the execution of MapReduce jobs for querying.
- Ecosystem and Tooling: Hadoop has a rich ecosystem of tools and frameworks such as Apache Spark, Apache Hive, and Apache Pig that can be used for various data processing tasks. It also has extensive support for data integration and processing. Druid, while it has a smaller ecosystem compared to Hadoop, provides built-in support for real-time dashboards and OLAP-style analytics.
- Data Updates: Druid allows for real-time updates and incremental ingestion of data streams while maintaining query performance. Hadoop typically requires complete reprocessing of data for updates, making it more suitable for batch data processing rather than real-time data analysis.
In Summary, Druid and Hadoop differ in their focus on real-time query capabilities, data storage and compression techniques, data ingestion mechanisms, querying capabilities, ecosystem and tooling support, and handling of data updates.
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.
Pros of Druid
- Real Time Aggregations15
- Batch and Real-Time Ingestion6
- OLAP5
- OLAP + OLTP3
- Combining stream and historical analytics2
- OLTP1
Pros of Hadoop
- Great ecosystem39
- One stack to rule them all11
- Great load balancer4
- Amazon aws1
- Java syntax1
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Cons of Druid
- Limited sql support3
- Joins are not supported well2
- Complexity1
Cons of Hadoop
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