From 33483172b6d867a6d2c5b097511dd1576af7f2f4 Mon Sep 17 00:00:00 2001 From: Jagdeep Date: Thu, 7 Jan 2021 18:02:11 +0800 Subject: [PATCH] Format Fix --- .../emr_instance_fleets.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md index ce73f974..2c481214 100644 --- a/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md +++ b/content/running_spark_apps_with_emr_on_spot_instances/emr_instance_fleets.md @@ -14,8 +14,8 @@ With EMR instance fleets, you specify target capacities for On-Demand Instances While a cluster is running, if Amazon EC2 reclaims a Spot Instance or if an instance fails, Amazon EMR tries to replace the instance with any of the instance types that you specify in your fleet. This makes it easier to regain capacity in case some of the instances get interrupted by EC2 when it needs the Spot capacity back.\ These options do not exist within the default EMR configuration option "Uniform Instance Groups", hence we will be using EMR Instance Fleets only. -As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version **5.12.1 and later**. With allocation strategy -/ -* On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first./ +As an enhancement to the default EMR instance fleets cluster configuration, the allocation strategy feature is available in EMR version **5.12.1 and later**. With allocation strategy -\ +* On-Demand instances use a lowest-price strategy, which launches the lowest-priced instances first.\ * Spot instances use a [capacity-optimized] (https://aws.amazon.com/about-aws/whats-new/2020/06/amazon-emr-uses-real-time-capacity-insights-to-provision-spot-instances-to-lower-cost-and-interruption/) allocation strategy, which allocates instances from most-available Spot Instance pools and lowers the chance of interruptions. This allocation strategy is appropriate for workloads that have a higher cost of interruption such as persistent EMR clusters running Apache Spark, Apache Hive, and Presto. {{% notice note %}}