Apache Druid vs. H2 Database Engine

It is extremely well suited to rapid ingest of data from large data sources, due to the fact that you can restrict what is ingested by column/field, so that you only pull in the data you actually want or need.

As stated earlier, the open source version could use better cluster management tools, and troubleshooting tools for failing jobs/tasks.

For running application tests it's well suited. H2 [Database Engine] can replace the real-world database solution for them easily and removes the requirement to set up a a separate database instance just for running unit tests. For using in actual production application one needs to consider scale. H2 is suitable if application runs in single instance and database is located in same machine as a file where that application runs. This means the application shouldn't have a large user base. However it's easy to switch to an actual MySQL instance if the need arises, it's most likely only a configuration change and doesn't require new code.

Incentivized

• Rapid ingest
• Limiting ingest to only the relevant fields/columns
• Easy ingest spec creation

• Can run as an in-memory database.
• Simple and quick to get started with, and is light weight (only 2MB).
• SQL compliant so it compatible with most relational databases.

Incentivized

• Security configuration is problematic
• Cluster management could have more features
• Troubleshooting incomplete tasks/jobs is a chore

• There's a warning in official FAQ "Is it Reliable?"-section which makes it seem like H2 is not yet a mature product.
• If raw SQL queries are used there maybe be differences between MySQL & H2. ORM library should be used.
• Support seems to be community-based only.

Incentivized

While both can run as an in-memory database, H2 Database Engine was just so much easier for us to use since we primarily use the Java stack and H2 Database Engine is also built with Java.

Incentivized

• Integration with S3 storage has saved about 35% on our storage, over HDFS
• The rapid ingest has saved user's time in the query aspects of their applications.
• The ability to ingest from a variety of data sources has made overall user application queries much simpler

• Doesn't take time from developers, once it's configs are set up for testing it works in everyone's development environments
• Easy to integrate in application, no need to setup separate database software, no maintenance
• No need to deal with infrastructure related issues/costs - database runs in same machine as the application that uses it.

Incentivized