From c2020437d9d63bb339184ca34671d40d1fc6d5f4 Mon Sep 17 00:00:00 2001 From: easyscheduler Date: Wed, 17 Jul 2019 20:08:26 +0800 Subject: [PATCH] Update README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 9d45e887e1..5aeac66b6c 100644 --- a/README.md +++ b/README.md @@ -30,20 +30,20 @@ Its main objectives are as follows:   | EasyScheduler | Azkaban | Airflow -- | -- | -- | -- -Stability |   |   |   +**Stability** |   |   |   Single point of failure | Decentralized multi-master and multi-worker | Yes Single Web and Scheduler Combination Node | Yes. Single Scheduler Additional HA requirements | Not required (HA is supported by itself) | DB | Celery / Dask / Mesos + Load Balancer + DB Overload processing | Task queue mechanism, the number of schedulable tasks on a single machine can be flexibly configured, when too many tasks will be cached in the task queue, will not cause machine jam. | Jammed the server when there are too many tasks | Jammed the server when there are too many tasks -Easy to use |   |   |   +**Easy to use** |   |   |   DAG Monitoring Interface | Visualization process defines key information such as task status, task type, retry times, task running machine, visual variables and so on at a glance. | Only task status can be seen | Can't visually distinguish task types Visual process definition | Yes All process definition operations are visualized, dragging tasks to draw DAGs, configuring data sources and resources. At the same time, for third-party systems, the api mode operation is provided. | No DAG and custom upload via custom DSL | No DAG is drawn through Python code, which is inconvenient to use, especially for business people who can't write code. Quick deployment | One-click deployment | Complex clustering deployment | Complex clustering deployment -Features |   |   |   +**Features** |   |   |   Suspend and resume | Support pause, recover operation | No Can only kill the workflow first and then re-run | No Can only kill the workflow first and then re-run Whether to support multiple tenants | Users on easyscheduler can achieve many-to-one or one-to-one mapping relationship through tenants and Hadoop users, which is very important for scheduling large data jobs. " Supports traditional shell tasks, while supporting large data platform task scheduling: MR, Spark, SQL (mysql, postgresql, hive, sparksql), Python, Procedure, Sub_Process | No | No Task type | Supports traditional shell tasks, and also support big data platform task scheduling: MR, Spark, SQL (mysql, postgresql, hive, sparksql), Python, Procedure, Sub_Process | shell、gobblin、hadoopJava、java、hive、pig、spark、hdfsToTeradata、teradataToHdfs | BashOperator、DummyOperator、MySqlOperator、HiveOperator、EmailOperator、HTTPOperator、SqlOperator Compatibility | Support the scheduling of big data jobs like spark, hive, Mr. At the same time, it is more compatible with big data business because it supports multiple tenants. | Because it does not support multi-tenant, it is not flexible enough to use business in big data platform. | Because it does not support multi-tenant, it is not flexible enough to use business in big data platform. -Scalability |   |   |   +**Scalability** |   |   |   Whether to support custom task types | Yes | Yes | Yes Is Cluster Extension Supported? | Yes The scheduler uses distributed scheduling, and the overall scheduling capability will increase linearly with the scale of the cluster. Master and Worker support dynamic online and offline. | Yes, but complicated Executor horizontal extend | Yes, but complicated Executor horizontal extend