surekhasaharan commented on a change in pull request #6122: New docs intro
URL: https://github.com/apache/incubator-druid/pull/6122#discussion_r208769975
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File path: docs/content/design/index.md
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@@ -2,152 +2,193 @@
layout: doc_page
---
-# Druid Concepts
+# What is Druid?
+
+Druid is a data store designed for high-performance slice-and-dice analytics
+("[OLAP](http://en.wikipedia.org/wiki/Online_analytical_processing)"-style) on
large data sets. Druid is most often
+used as a data store for powering GUI analytical applications, or as a backend
for highly-concurrent APIs that need
+fast aggregations. Common application areas for Druid include:
+
+- Clickstream analytics
+- Network flow analytics
+- Server metrics storage
+- Application performance metrics
+- Digital marketing analytics
+- Business intelligence / OLAP
+
+Druid's key features are:
+
+1. **Columnar storage format.** Druid uses column-oriented storage, meaning it
only needs to load the exact columns
+needed for a particular query. This gives a huge speed boost to queries that
only hit a few columns. In addition, each
+column is stored optimized for its particular data type, which supports fast
scans and aggregations.
+2. **Scalable distributed system.** Druid is typically deployed in clusters of
tens to hundreds of servers, and can
+offer ingest rates of millions of records/sec, retention of trillions of
records, and query latencies of sub-second to a
+few seconds.
+3. **Massively parallel processing.** Druid can process a query in parallel
across the entire cluster.
+4. **Realtime or batch ingestion.** Druid can ingest data either realtime
(ingested data is immediately available for
+querying) or in batches.
+5. **Self-healing, self-balancing, easy to operate.** As an operator, to scale
the cluster up or down, simply add or
+remove servers and the cluster will rebalance itself automatically, in the
background, without any downtime. If any
+Druid servers fail, the system will automatically route around the damage
until those servers can be replaced. Druid
+is designed to run 24/7 with no need for planned downtimes for any reason,
including configuration changes and software
+updates.
+6. **Cloud-native, fault-tolerant architecture that won't lose data.** Once
Druid has ingested your data, a copy is
+stored safely in [deep storage](#deep-storage) (typically cloud storage, HDFS,
or a shared filesystem). Your data can be
+recovered from deep storage even if every single Druid server fails. For more
limited failures affecting just a few
+Druid servers, replication ensures that queries are still possible while the
system recovers.
+7. **Indexes for quick filtering.** Druid uses
[CONCISE](https://arxiv.org/pdf/1004.0403) or
+[Roaring](https://roaringbitmap.org/) compressed bitmap indexes to create
indexes that power fast filtering and
+searching across multiple columns.
+8. **Approximate algorithms.** Druid includes algorithms for approximate
count-distinct, approximate ranking, and
+computation of approximate histograms and quantiles. These algorithms offer
bounded memory usage and are often
+substantially faster than exact computations. For situations where exactness
is more important than speed, Druid also
Review comment:
Might be better to write as `where accuracy is more important than speed`
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