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Posted to commits@hugegraph.apache.org by ji...@apache.org on 2022/11/23 15:36:42 UTC
[incubator-hugegraph-computer] branch master updated: doc: improve README.md (#198)
This is an automated email from the ASF dual-hosted git repository.
jin pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-hugegraph-computer.git
The following commit(s) were added to refs/heads/master by this push:
new 7744295f doc: improve README.md (#198)
7744295f is described below
commit 7744295fa855a861307cdf67106a6be7fde8f5eb
Author: Jermy Li <je...@apache.org>
AuthorDate: Wed Nov 23 23:36:36 2022 +0800
doc: improve README.md (#198)
---
README.md | 11 ++++++-----
1 file changed, 6 insertions(+), 5 deletions(-)
diff --git a/README.md b/README.md
index 769b5b9c..db1430c7 100644
--- a/README.md
+++ b/README.md
@@ -5,15 +5,16 @@
[](https://codecov.io/gh/apache/hugegraph-computer)
[](https://hub.docker.com/repository/docker/hugegraph/hugegraph-builtin-algorithms)
-hugegraph-computer is a distributed graph processing system for hugegraph. It is an implementation of [Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf). It runs on Kubernetes or YARN framework.
+The hugegraph-computer is a distributed graph processing system for hugegraph. It is an implementation of [Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf). It runs on Kubernetes or YARN framework.
## Features
-- Based on BSP(Bulk Synchronous Parallel) model, every iteration is a superstep.
-- Auto memory management. The framework will split some data to disk, the framework will never OOM(Out of Memory).
+- Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage.
+- Based on BSP(Bulk Synchronous Parallel) model, an algorithm performs computing through multiple parallel iterations, every iteration is a superstep.
+- Auto memory management. The framework will never be OOM(Out of Memory) since it will split some data to disk if it doesn't have enough memory to hold all the data.
- The part of edges or the messages of super node can be in memory, so you will never lose it.
-- You can output the result to HDFS or HugeGraph, or any other system.
-- Easy to develop a new algorithm. You need to focus on a vertex only, not to worry about messages transferring and memory.
+- You can output the results to HDFS or HugeGraph, or any other system.
+- Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management.
## Learn More