Apache Druid vs. FeatureBase
Apache Druid for Real-Time Analytics:
Apache Druid is a real-time analytics database designed for OLAP queries on large data sets and optimized for event-oriented data. Most often, Druid powers use cases where real-time ingestion, fast query performance, and high uptime are essential. Druid is commonly used as the database backend for GUIs of analytical applications or for highly concurrent APIs that need fast aggregations. Druid works best with event-oriented data.
As organizations progress in analytical maturity and their volume of data increases, they typically see Druid hardware costs increase significantly. As a result, achieving real-time decisions may require considerably more hardware resources, especially as workloads continue to scale. Unfortunately, this limitation often leaves organizations unable to perform real-time analytics cost-effectively.
Reasons to Consider Replacing Apache Druid:
- Your server costs to power your workloads have gotten out of control.
- You need to perform complex JOINs on many different tables of data or very large tables (10s-100 billions of rows).
- You regularly perform streaming updates.
- You’re trying to deliver low-latency, high-throughput, and highly concurrent workloads with the freshest data.
There is another option available to help you achieve your goals without making costly tradeoffs.
FeatureBase is a feature-oriented database platform that makes an organization’s freshest data immediately accessible, actionable, and reusable. FeatureBase powers real-time analytics and machine learning applications by executing low-latency, high-throughput, and highly concurrent workloads simultaneously.
Apache Druid vs. FeatureBase:
Druid and Molecula have key technical differences, including data ingestion, query capabilities, and data modeling. Let’s look at each.
Real-Time Data Ingestion:
Druid is optimized to provide analytics against massive quantities of streaming data. If you need low-latency updates of existing records using a primary key, however, Druid might not be the best choice for you. Druid supports streaming inserts but not streaming updates. Updates must be performed via background batch jobs; updating Druid is costly and can impact performance. If you need to make frequent updates to your data, Druid may not be for you unless you can adjust your update process.
FeatureBase can handle the ingestion of massive-scale streaming data while simultaneously allowing for real-time inserts and updates to existing data schemas.
Druid is a read-oriented analytical database, which means its write semantics are not highly fluid. While Druid supports full inner JOINs, when it comes to outer JOINs, your best bet is to leave those to the data warehouse in your stack.
FeatureBase is also optimized for reads, but it is extremely good at supporting live updates while maintaining low-latency queries. Additionally, because of our ability to collapse multiple tables into single entities and allow for multiple values within single fields, we eliminate the need for data preaggregation (JOINs), allowing organizations to operate on their freshest data while maintaining ultra low latency.
Druid is a column-oriented database that uses indexing structures to speed up query execution when a filter is provided. However, indexing structures on top of column-oriented databases increase storage overhead (and make it more challenging to allow for mutation).
FeatureBase takes a different approach to data modeling. Tables are typically modeled around entities (customers, patients, unique IDs, etc.) or events (transactions, etc.). In addition, tables can have multiple sources (batch and streaming) and update records or add new fields in real time.
Mapping relational tables to FeatureBase can be as simple as a one-to-one mapping, but major performance improvements can be made through the mapping and feature table structure depending on:
- the expected query workload
- the type, size, and cardinality of the data
- the cost requirements
How to Reduce Your Number of Druid Servers:
Molecula FeatureBase is a feature-oriented database platform service purpose-built for real-time analytics and machine learning. FeatureBase continuously extracts and updates features from streaming technologies like Kafka and other data sources without the need for staging or preaggregation. This superpower allows FeatureBase to serve real-time applications and analytical workloads using 50-90% fewer servers than Apache Druid.
While FeatureBase and Apache Druid provide similar real-time analytics functionality at a surface level, when you dig a bit deeper, you can see that FeatureBase’s feature-oriented data format allows for some major gains in latency, freshness, and hardware footprint to power real-time use cases.