Distributed System support

CHANGELOG.md

@@ -2,6 +2,21 @@
 
 #### Version 0.12.0 (TBD)
 
+##### Distributed Concourse (ALPHA)
+* **Added support for running Concourse as a distributed cluster.** This ALPHA release allows Concourse to operate across multiple nodes while maintaining strong consistency guarantees.
+* Distributed Concourse operates as a CP system with optimistic availability, meaning it prioritizes data consistency while maximizing availability when possible.
+* Each node in the cluster operates as a peer - there is no single leader, allowing clients to connect to any node to perform operations.
+* Distributed deployment can be enabled by configuring the new `cluster` settings in your configuration file:
+  * `cluster.nodes`: A list of `host:port` entries for each node in the cluster
+  * `cluster.replication_factor`: Specifies the minimum number of nodes that should receive a copy of each piece of data (defaults to n/2 + 1, where n is the number of nodes)
+* **IMPORTANT**: This is an ALPHA release with several limitations:
+  * Cannot convert an existing single-node deployment with data into a distributed cluster
+  * Cannot have nodes with different configurations within the same cluster
+  * Cannot have nodes running different Concourse versions
+  * Cannot add or remove nodes from an existing cluster dynamically
+  * Not recommended for production systems or mission-critical data
+* The focus of this initial implementation is on core correctness of the distributed system protocol, data routing, and consistency - consider it a proof of concept at this stage.
+
 ##### Batch Transporter for Improved Automatic Indexing
 
 We've introduced a new mechanism to control how Concourse Server transports data from the Buffer to the Database, significantly improving system throughput and responsiveness:

README.md

@@ -103,13 +103,13 @@ select(keys=["name", "team"], criteria="age bw 22 29")
 ###### You can even query data from the past without doing any extra work.
 ```groovy
 // Return data from 04/2009 from records that match now
-get(key="age", record=durant, time=time("04/2009")) 
+get(key="age", record=durant, time=time("04/2009"))
 
 // Return records that matched in 2011
-find("team = Chicago Bulls at 2011") 
+find("team = Chicago Bulls at 2011")
 
 // Return data from two years ago from records that match now
-select(criteria="age > 25 and team != Chicago Bulls", time=time("two years ago")) 
+select(criteria="age > 25 and team != Chicago Bulls", time=time("two years ago"))
 ```
 ###### It is very easy to analyze how data has changed over time and revert to previous states.
 ```groovy
@@ -133,6 +133,35 @@ stage({
 ```
 You can find more examples in the [examples](examples) directory. More information is also available in the [Concourse Guide](https://docs.cinchapi.com/concourse).
 
+## Distributed Concourse
+
+Concourse can operate as a distributed system, allowing you to run a cluster of Concourse nodes that work together to provide improved fault tolerance and reliability.
+
+### Key Features
+
+* **Peer-to-peer architecture**: Every node in a Concourse cluster is a peer - there is no designated leader. Clients can connect to any node to perform operations.
+* **Strong consistency**: Distributed Concourse is a CP system that prioritizes data consistency while maximizing availability when possible through optimistic availability techniques.
+* **Simple configuration**: Enable distribution by providing cluster information in your configuration:
+  ```yaml
+  cluster:
+    nodes:
+      - host1:1717
+      - host2:1717
+      - host3:1717
+    replication_factor: 2  # Defaults to n/2+1 if not specified
+  ```
+
+### Developer Utilities
+
+For development and testing, you can use the included utility script to quickly spin up a local cluster:
+
+```bash
+# Start a 3-node cluster running on ports 1717, 1718, and 1719
+./utils/start-cluster.sh -n 1717 -n 1718 -n 1719
+```
+
+This creates a fully functional Concourse cluster on your local machine, perfect for testing distributed features or simulating various cluster scenarios during development.
+
 ## Motivation
 Whether you use SQL or NoSQL, it's hard to get real-time insight into your mission critical data because most systems are only optimized for either transactions or analytics, not both. As a result, end-to-end data management requires complex data pipelining, which slows down development, complicates infrastructure and increases costs.