A Druid cluster consists of various node types that need to be set up depending on your use case. See our [Design](Design.html) docs for a description of the different node types.
As a special case, the absolute minimum setup is one of the standalone examples for real-time ingestion and querying; see [Examples](Examples.html) that can easily run on one machine with one core and 1GB RAM. This layout can be set up to try some basic queries with Druid.
This layout can be used to load some data from deep storage onto a Druid historical node for the first time. A minimal physical layout for a 1 or 2 core machine with 4GB of RAM is:
The machine size "flavors" are using AWS/EC2 terminology for descriptive purposes only and is not meant to imply that AWS/EC2 is required or recommended. Another cloud provider or your own hardware can also work.
A minimal physical layout not constrained by cores that demonstrates parallel querying and realtime, using AWS-EC2 "small"/m1.small (one core, with 1.7GB of RAM) or larger, no real-time, is:
This layout naturally lends itself to adding more RAM and core to Historical nodes, and to adding many more Historical nodes. Depending on the actual load, the Coordinator, metadata server, and Zookeeper might need to use larger machines.
The machine size "flavors" are using AWS/EC2 terminology for descriptive purposes only and is not meant to imply that AWS/EC2 is required or recommended. Another cloud provider or your own hardware can also work.
The Historical and Broker nodes will use as many cores as are available, depending on usage, so it is best to keep these on dedicated machines. The upper limit of effectively utilized cores is not well characterized yet and would depend on types of queries, query load, and the schema. Historical daemons should have a heap a size of at least 1GB per core for normal usage, but could be squeezed into a smaller heap for testing. Since in-memory caching is essential for good performance, even more RAM is better. Broker nodes will use RAM for caching, so they do more than just route queries.
The effective utilization of cores by Zookeeper, MySQL, and Coordinator nodes is likely to be between 1 and 2 for each process/daemon, so these could potentially share a machine with lots of cores. These daemons work with heap a size between 500MB and 1GB.
Indexed segments should be kept in a permanent store accessible by all nodes like AWS S3 or HDFS or equivalent. Refer to [Deep-Storage](deep-storage.html) for more details on supported storage types.
Local disk ("ephemeral" on AWS EC2) for caching is recommended over network mounted storage (example of mounted: AWS EBS, Elastic Block Store) in order to avoid network delays during times of heavy usage. If your data center is suitably provisioned for networked storage, perhaps with separate LAN/NICs just for storage, then mounted might work fine.
Setting up a cluster is essentially just firing up all of the nodes you want with the proper [configuration](Configuration.html). One thing to be aware of is that there are a few properties in the configuration that potentially need to be set individually for each process:
`druid.server.type` should be set to "historical" for your historical nodes and realtime for the realtime nodes. The Coordinator will only assign segments to a "historical" node and the broker has some intelligence around its ability to cache results when talking to a realtime node. This does not need to be set for the coordinator or the broker.
`druid.host` should be set to the hostname and port that can be used to talk to the given server process. Basically, someone should be able to send a request to http://${druid.host}/ and actually talk to the process.
`druid.port` should be set to the port that the server should listen on. In the vast majority of cases, this port should be the same as what is on `druid.host`.
The simplest way to build and run from the repository is to run `mvn package` from the base directory and then take `druid-services/target/druid-services-*-selfcontained.jar` and push that around to your machines; the jar does not need to be expanded, and since it contains the main() methods for each kind of service, it is *not* invoked with java -jar. It can be run from a normal java command-line by just including it on the classpath and then giving it the main class that you want to run. For example one instance of the Historical node/service can be started like this:
