mirror of https://github.com/apache/druid.git
15 lines
1.3 KiB
Markdown
15 lines
1.3 KiB
Markdown
---
|
||
layout: doc_page
|
||
---
|
||
|
||
Hadoop has shown the world that it’s possible to house your data warehouse on commodity hardware for a fraction of the price of typical solutions. As people adopt Hadoop for their data warehousing needs, they find two things.
|
||
|
||
1. They can now query all of their data in a fairly flexible manner and answer any question they have
|
||
2. The queries take a long time
|
||
|
||
The first one is the joy that everyone feels the first time they get Hadoop running. The latter is what they realize after they have used Hadoop interactively for a while because Hadoop is optimized for throughput, not latency.
|
||
|
||
Druid is a complementary addition to Hadoop. Hadoop is great at storing and making accessible large amounts of individually low-value data. Unfortunately, Hadoop is not great at providing query speed guarantees on top of that data, nor does it have very good operational characteristics for a customer-facing production system. Druid, on the other hand, excels at taking high-value summaries of the low-value data on Hadoop, making it available in a fast and always-on fashion, such that it could be exposed directly to a customer.
|
||
|
||
Druid also requires some infrastructure to exist for [deep storage](Deep-Storage.html). HDFS is one of the implemented options for this [deep storage](Deep-Storage.html).
|