• MapReduce Service

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  1. Help Center
  2. MapReduce Service
  3. User Guide
  4. Cluster Operation Guide
  5. Creating a Cluster

Creating a Cluster

Procedure

  1. Log in to the MRS management console.
  2. Click  in the upper-left corner on the management console and select Region and Project.
  3. Click Create Cluster and open the Create Cluster page.

    NOTE:

    Note the usage of quotas when you create a cluster. If the resource quotas are insufficient, apply for new quotas based on the prompted information and create new clusters.

  4. Table 1Table 2Table 3Table 4Table 5, and Table 6 describe the basic configuration information, node configuration information, login information, component information and job configuration information for a cluster, respectively.

    Table 1 Basic cluster configuration information

    Parameter

    Description

    Region

    To change the region, click in the upper left corner to select one.

    AZ

    An availability zone (AZ) is a physical area that uses independent power and network resources. In this way, applications are interconnected using internal networks but are physically isolated. As a result, application availability is improved. It is recommended that you create clusters under different AZs.

    MRS enables an AZ to be randomly selected to prevent excessive VMs to be created in the specified default AZ and avoid uneven resource occupation among AZs. MRS also enables a tenant's all VMs to be created in one AZ as much as possible.

    If your VMs must be located in different AZs, specify AZs when creating VMs. In a multi-user and multi-AZ scenario, each user tries to obtain a default AZ that is different from other users' default AZs.

    Select an AZ of the region in the cluster. Currently, only the eu-de region is supported.

    Cluster Name

    Cluster name, which is globally unique.

    A cluster name can contain only 1 to 64 characters, including letters, digits, hyphens (-), or underscores (_).

    The default name is mrs_xxxx, where xxxx is a random combination of four letters and numbers.

    Cluster Version

    Currently, MRS 1.3.0, MRS 1.5.0 , MRS 1.6.0, MRS 1.6.3 and MRS 1.7.2 are supported.

    The latest version of MRS is used by default.

    Kerberos Authentication

    Indicates whether to enable Kerberos authentication when logging in to MRS Manager. Possible values are as follows:

    • If Kerberos authentication is disabled, you can use all functions of an MRS cluster. You are advised to disable Kerberos authentication in single-user scenarios. For clusters with Kerberos authentication disabled, you can directly access the MRS cluster management page and components without security authentication. If Kerberos authentication is disabled, you can follow instructions in Security Configuration Suggestions for Clusters with Kerberos Authentication Disabled to perform security configuration.

    • If Kerberos authentication is enabled, common users cannot use the file management and job management functions of an MRS cluster and cannot view cluster resource usage or the job records for Hadoop and Spark. To use more cluster functions, the users must contact the MRS Manager administrator to assign more permissions. You are advised to enable Kerberos authentication in multi-user scenarios.

    You can click  or  to disable or enable Kerberos authentication, respectively.

    After creating MRS clusters with Kerberos authentication enabled, users can manage running clusters on MRS Manager. The users must prepare a working environment on the public cloud platform for accessing MRS Manager. For details, see Accessing MRS Manager Supporting Kerberos Authentication.

    NOTE:

    The Kerberos AuthenticationUsernamePassword, and Confirm Password parameters are displayed only after the user obtains the permission for MRS in security mode.

    Username

    Indicates the username for the administrator of MRS Manager. admin is used by default.

    This parameter needs to be configured only when Kerberos Authentication is set to "Enable": .

    Password

    Indicates the password of the MRS Manager administrator.

    • Must contain 8 to 32 characters.
    • Must contain at least three types of the following:
      • Lowercase letters
      • Uppercase letters
      • Digits
      • Special characters of `~!@#$%^&*()-_=+\|[{}];:'",<.>/?
      • Spaces
    • Must be different from the username.
    • Must be different from the username written in reverse order.

    Password strength: The colorbar in red, orange, and green indicates weak, medium, and strong password, respectively.

    This parameter needs to be configured only when Kerberos Authentication is set to "Enable": .

    Confirm Password

    Enter the user password again.

    This parameter needs to be configured only when Kerberos Authentication is set to "Enable": .

    Cluster Type

    MRS 1.3.0 or later provides two types of clusters:
    • Analysis cluster: is used for offline data analysis and provides Hadoop components.
    • Streaming cluster: is used for streaming tasks and provides stream processing components.
    NOTE:

    MRS streaming clusters do not support Job Management or File Management. If the cluster type is Streaming Cluster, the Create Job area is not displayed on the cluster creation page.

    Component

    • MRS 1.7.2 supports the following components:
      Components of an analysis cluster:
      • Hadoop 2.8.3: distributed system architecture
      • Spark 2.2.1: in-memory distributed computing framework
      • HBase 1.3.1: distributed column store database
      • Hive 1.2.1: data warehouse framework built on Hadoop
      • Hue 3.11.0: providing the Hadoop UI capability, which enables users to analyze and process Hadoop cluster data on browsers
      • Loader 2.0.0: a tool based on source sqoop 1.99.7, designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.

        Hadoop is mandatory, and Spark and Hive must be used together. Select components based on services.

      Components of a streaming cluster:
      • Kafka 0.10.2.0: distributed message subscription system
      • Storm 1.0.2: distributed real-time computing system
      • Flume 1.6.0: a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.
    • MRS 1.6.3 supports the following components:
      Components of an analysis cluster:
      • Hadoop 2.7.2: distributed system architecture
      • Spark 2.1.0: in-memory distributed computing framework
      • HBase 1.3.1: distributed column store database
      • Hive 1.2.1: data warehouse framework built on Hadoop
      • Hue 3.11.0: providing the Hadoop UI capability, which enables users to analyze and process Hadoop cluster data on browsers
      • Loader 2.0.0: a tool based on source sqoop 1.99.7, designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.

        Hadoop is mandatory, and Spark and Hive must be used together. Select components based on services.

      Components of a streaming cluster:
      • Kafka 0.10.0.0: distributed message subscription system
      • Storm 1.0.2: distributed real-time computing system
      • Flume 1.6.0: a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.
    • MRS 1.6.0 supports the following components:
      Components of an analysis cluster:
      • Hadoop 2.7.2: distributed system architecture
      • Spark 2.1.1: in-memory distributed computing framework
      • HBase 1.3.1: distributed column store database
      • Hive 1.2.1: data warehouse framework built on Hadoop
      • Hue 3.11.0: providing the Hadoop UI capability, which enables users to analyze and process Hadoop cluster data on browsers
      • Loader 2.0.0: a tool based on source sqoop 1.99.7, designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.

        Hadoop is mandatory, and Spark and Hive must be used together. Select components based on services.

      Components of a streaming cluster:
      • Kafka 0.10.0.0: distributed message subscription system
      • Storm 1.0.2: distributed real-time computing system
      • Flume 1.6.0: a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.
    • MRS 1.5.0 supports the following components:
      Components of an analysis cluster:
      • Hadoop 2.7.2: distributed system architecture
      • Spark 2.1.0: in-memory distributed computing framework
      • HBase 1.0.2: distributed column store database
      • Hive 1.2.1: data warehouse framework built on Hadoop
      • Hue 3.11.0: providing the Hadoop UI capability, which enables users to analyze and process Hadoop cluster data on browsers
      • Loader 2.0.0: a tool based on source sqoop 1.99.7, designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.

        Hadoop is mandatory, and Spark and Hive must be used together. Select components based on services.

      Components of a streaming cluster:
      • Kafka 0.10.0.0: distributed message subscription system
      • Storm 1.0.2: distributed real-time computing system
      • Flume 1.6.0: a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.
    • MRS 1.3.0 supports the following components:
      Components of an analysis cluster:
      • Hadoop 2.7.2: distributed system architecture
      • Spark 1.5.1: in-memory distributed computing framework
      • HBase 1.0.2: distributed column store database
      • Hive 1.2.1: data warehouse framework built on Hadoop
      • Hue 3.11.0: providing the Hadoop UI capability, which enables users to analyze and process Hadoop cluster data on browsers

        Hadoop is mandatory, and Spark and Hive must be used together. Select components based on services.

        NOTE:

        After Kerberos Authentication is set to "Enable": , the Hue component can be selected, but the Create Job area is not displayed, indicating that jobs cannot be created.

      Components of a streaming cluster:
      • Kafka 0.10.0.0: distributed message subscription system
      • Storm 1.0.2: distributed real-time computing system

    VPC

    A VPC is a secure, isolated, and logical network environment.

    Select the VPC for which you want to create a cluster and click View VPC to view the name and ID of the VPC. If no VPC is available, create one.

    Subnet

    A subnet provides dedicated network resources that are isolated from other networks, improving network security.

    Select the subnet for which you want to create a cluster to enter the VPC and view the name and ID of the subnet.

    If no subnet is created under the VPC, click Create Subnet to create one.

    WARNING:

    Do not associate the subnet with the network ACL.

    Security Group

    A security group is a set of ECS access rules. It provides access policies for ECSs that have the same security protection requirements and are mutually trusted in a VPC.

    When you create an MRS cluster, you can select Auto Create from the drop-down list of Security Group to create a security group or select an existing security group.

    Cluster HA

    Cluster HA specifies whether to enable high availability for a cluster. This parameter is enabled by default.

    If you enable this option, the management processes of all components will be deployed on both Master nodes to achieve hot standby and prevent single-node failure, improving reliability. If you disable this option, they will be deployed on only one Master node. As a result, if a process of a component becomes abnormal, the component will fail to provide services.

    • : Disabled. When Cluster HA is disabled, there is only one Master node and the number of Core nodes is three by default. However, you can decrease the number of Core nodes to 1.
    • : Enabled. When Cluster HA is enabled, there are two Master nodes and the number of Core nodes is three by default. However, you can decrease the number of Core nodes to 1.

    You can click  or : to disable or enable high availability, respectively.

    Table 2 Cluster node information

    Parameter

    Description

    Type

    MRS provides three types of nodes:

    • Master: A Master node in an MRS cluster manages the cluster, assigns cluster executable files to Core nodes, traces the execution status of each job, and monitors the DataNode running status.
    • Core: A Core node in a cluster processes data and stores process data in HDFS.
    • Task: A Task node in a cluster is used for computing and does not store persistent data. Yarn and Storm are mainly installed on Task nodes. Task nodes are optional, and the number of Task nodes can be zero. (Task nodes are supported by MRS 1.6.3 or later.)
      When the number of clusters does not change much but the clusters' service processing capabilities need to be remarkably and temporarily improved, add Task nodes to address the following situations:
      • The volume of temporary services is increased, for example, report processing at the end of the year.
      • Long-term tasks must be completed in a short time, for example, some urgent analysis tasks.

    (Optional)Add Task Node

    Click Add Task Node to configure the information about the Task node.

    Click  behind Disabled in the row of Task. On the Auto Scaling page that is displayed, enable auto scaling. For details, see Performing Auto Scaling for a Cluster.

    Instance Specifications

    Instance specifications of a node

    MRS supports host specifications determined by CPU, memory, and disks space.

    • Master nodes support h1.2xlarge.4, h1.4xlarge.4, h1.8xlarge.4, c2.4xlarge, s1.4xlarge and s1.8xlarge, c3.2xlarge.2, c3.xlarge.4, c3.2xlarge.4, c3.4xlarge.2, c3.4xlarge.4, c3.8xlarge.4, c3.15xlarge.4.
    • Core nodes of a streaming cluster support s1.xlarge, c2.2xlarge, c2.4xlarge, s1.4xlarge, s1.8xlarge, d1.8xlarge, h1.2xlarge.4, h1.4xlarge.4 and h1.8xlarge.4, c3.2xlarge.2, c3.xlarge.4, c3.2xlarge.4, c3.4xlarge.2, c3.4xlarge.4, c3.8xlarge.4, c3.15xlarge.4.
    • Core nodes of an analysis cluster support all specifications c2.2xlarge, c2.4xlarge, s1.xlarge, s1.4xlarge, s1.8xlarge, d1.xlarge, d1.2xlarge, d1.4xlarge, d1.8xlarge, h1.2xlarge.4, h1.4xlarge.4 and h1.8xlarge.4, c3.2xlarge.2, c3.xlarge.4, c3.2xlarge.4, c3.4xlarge.2, c3.4xlarge.4, c3.8xlarge.4, c3.15xlarge.4, d2.xlarge.8, d2.2xlarge.8, d2.4xlarge.8, d2.8xlarge.8.
    • Task nodes support c2.2xlarge, c2.4xlarge, s1.xlarge, s1.4xlarge, s1.8xlarge, h1.2xlarge.4, h1.4xlarge.4 and h1.8xlarge.4, c3.2xlarge.2, c3.xlarge.4, c3.2xlarge.4, c3.4xlarge.2, c3.4xlarge.4, c3.8xlarge.4, c3.15xlarge.4.

    The following provides specification details.

    • General Computing S1 > 4 vCPUs 16 GB | s1.xlarge
      • CPU: 4-core
      • Memory: 16 GB
      • System Disk: 40 GB
    • Disk-intensive D1 > 4 vCPUs 32 GB | d1.xlarge
      • CPU: 4-core
      • Memory: 32 GB
      • System Disk: 40 GB
      • Data Disk: 1.8 TB x 3 HDDs
    • General Computing C2 > 8 vCPUs 16 GB | c2.2xlarge
      • CPU: 8-core
      • Memory: 16 GB
      • System Disk: 40 GB
    • Disk-intensive D1 > 8 vCPUs 64 GB | d1.2xlarge
      • CPU: 8-core
      • Memory: 64 GB
      • System Disk: 40 GB
      • Data Disk: 1.8 TB x 6 HDDs
    • General Computing C2 > 16 vCPUs 32 GB | c2.4xlarge
      • CPU: 16-core
      • Memory: 32 GB
      • System Disk: 40 GB
    • General Computing S1 > 16 vCPUs 64 GB | s1.4xlarge
      • CPU: 16-core
      • Memory: 64 GB
      • System Disk: 40 GB
    • Disk-intensive D1 > 16 vCPUs 128 GB | d1.4xlarge
      • CPU: 16-core
      • Memory: 128 GB
      • System Disk: 40 GB
      • Data Disk: 1.8 TB x 12 HDDs
    • General Computing S1 > 32 vCPUs 128 GB | s1.8xlarge
      • CPU: 32-core
      • Memory: 128 GB
      • System Disk: 40 GB
    • General computing-plus kvm C3 > 4 vCPUs 16 GB | c3.xlarge.4
      • CPU: 4-core
      • Memory: 16 GB
      • System Disk: 40 GB
    • General computing-plus kvm C3 > 8 vCPUs 32 GB | c3.2xlarge.4
      • CPU: 8-core
      • Memory: 32 GB
      • System Disk: 40 GB
    • General computing-plus kvm C3 > 16 vCPUs 32 GB | c3.4xlarge.2
      • CPU: 16-core
      • Memory: 32 GB
      • System Disk: 40 GB
    • General computing-plus kvm C3 > 16 vCPUs 64 GB | c3.4xlarge.4
      • CPU: 16-core
      • Memory: 64 GB
      • System Disk: 40 GB
    • General computing-plus kvm C3 > 32 vCPUs 128 GB | c3.8xlarge.4
      • CPU: 32-core
      • Memory: 128 GB
      • System Disk: 40 GB
    • General computing-plus kvm C3 > 60 vCPUs 256 GB | c3.15xlarge.4
      • CPU: 60-core
      • Memory: 256 GB
      • System Disk: 40 GB
    • General computing-plus kvm C3 > 8 vCPUs 16 GB | c3.2xlarge.2
      • CPU: 8-core
      • Memory: 16 GB
      • System Disk: 40 GB
    • Disk-intensive D1 > 36 vCPUs 256 GB | d1.8xlarge
      • CPU: 36-core
      • Memory: 256 GB
      • System Disk: 40 GB
      • Data Disk: 1.8 TB x 24 HDDs
    • High-Performance Computing H1 > 8 vCPUs 32 GB | h1.2xlarge
      • CPU: 8-core
      • Memory: 32 GB
      • System Disk: 40 GB
    • High-Performance Computing H1 > 16 vCPUs 64 GB | h1.4xlarge
      • CPU: 16-core
      • Memory: 64 GB
      • System Disk: 40 GB
    • High-Performance Computing H1 > 32 vCPUs 128 GB | h1.8xlarge
      • CPU: 32-core
      • Memory: 128 GB
      • System Disk: 40 GB
    • Disk-intensive kvm D2 > 4 vCPUs 32 GB | d2.xlarge.8
      • CPU: 4-core
      • Memory: 32 GB
      • System Disk: 40 GB
      • Data Disk: 1.8 TB x 2 HDDs
    • Disk-intensive kvm D2 > 8 vCPUs 64 GB | d2.2xlarge.8
      • CPU: 8-core
      • Memory: 64 GB
      • System Disk: 40 GB
      • Data Disk: 1.8 TB x 4 HDDs
    • Disk-intensive kvm D2 > 16 vCPUs 128 GB | d2.4xlarge.8
      • CPU: 16-core
      • Memory: 128 GB
      • System Disk: 40 GB
      • Data Disk: 1.8 TB x 8 HDDs
    • Disk-intensive kvm D2 > 32 vCPUs 256 GB | d2.8xlarge.8
      • CPU: 32-core
      • Memory: 256 GB
      • System Disk: 40 GB
      • Data Disk: 1.8 TB x 16 HDDs
    NOTE:
    • More advanced instance specifications allow better data processing.
    • If the specifications of Core nodes are d1.xlarge, d1.2xlarge, d1.4xlarge, or d1.8xlarge, Data Disk is not displayed. This is because data disks are configured by default for these specifications. Other specifications do not have data disks. Users must manually add data disks if they are required.
    • If you select HDDs for Core nodes, there is no charging information for data disks. The fees are charged with ECSs.
    • If you select HDDs for Core nodes, the system disks (40 GB) of Master nodes and Core nodes, as well as the data disks (200 GB) of Master nodes, are SATA disks.
    • If you select non-HDD disks for Core nodes, the disk types of Master and Core nodes are determined by Data Disk.
    • If Sold Out appears next to an instance specification of a node, the node of this specification cannot be purchased. You can only purchase nodes of other specifications.

    Quantity

    Number of Master, Core and Task nodes

    For Master nodes:

    • If Cluster HA is enabled, the number of Master nodes is fixed to 2.
    • If Cluster HA is disabled, the number of Master nodes is fixed to 1.

    The minimum number of Core nodes is 1 and the total number of Core and Task nodes cannot exceed 500.

    NOTE:
    • If more than 500 Core nodes or Task nodes are required, contact technical support engineers or invoke a background interface to modify the database.
    • A small number of nodes may cause clusters to run slowly. Set an appropriate value based on data to be processed.

    Storage Type

    Disk storage type

    The following disk types are supported:

    • SATA: Common I/O
    • SAS: High I/O
    • SSD: Ultra-high I/O

    Storage Space (GB)

    Disk space of MRS

    Users can add disks to increase storage capacity when creating a cluster. There are two different configurations for storage and computing:

    • Data storage and computing are performed separately

      Data is stored in OBS, which features low cost and unlimited storage capacity. The clusters can be terminated at any time in OBS. The computing performance is determined by OBS access performance and is lower than that of HDFS. This configuration is recommended if data computing is infrequent.

    • Data storage and computing are performed together

      Data is stored in HDFS, which features high cost, high computing performance, and limited storage capacity. Before terminating clusters, you must export and store the data. This configuration is recommended if data computing is frequent.

    The disk sizes range from 100 GB to 32000 GB, with 10 GB added each time, for example, 100 GB, 110 GB.

    NOTE:
    • The Master node increases data disk storage space for MRS Manager. The disk type must be the same as the data disk type of Core nodes. The default disk space is 200 GB and cannot be changed.
    • When the specifications of Core nodes are d1.xlarge, d1.2xlarge, d1.4xlarge, or d1.8xlarge, Data Disk is not displayed. This applies to MRS 1.6.0 or earlier.

    Data Disk

    Number of data disks on Master, Core, and Task nodes

    Master: currently fixed at 1

    Core: 1 to 10

    Task: 0 to 10

    Auto Scaling

    After you have added a Task node, if you have not enabled the Auto Scaling function, Disabled is displayed in the Auto Scaling column. If you have enabled the Auto Scaling function, Minimum xxx and Maximum xxx are displayed in the Auto Scaling column. xxx indicates the number of nodes. Click  . On the Auto Scaling page that is displayed, you can determine whether to enable or disable the Auto Scaling function and configure the auto scaling rule. For details, see Performing Auto Scaling for a Cluster.

    Operation

    Only after you click Add Task Node, Task nodes can be configured.

    If you do not need to configure a Task node, click Delete in the row of the Task node.

    Table 3 Login information

    Parameter

    Description

    Key Pair

    Keys are used to log in to Master1 of the cluster.

    A key pair, also called an SSH key, consists of a public key and a private key. You can create an SSH key and download the private key for authenticating remote login. For security, a private key can only be downloaded once. Keep it secure.

    Select the key pair, for example, SSHkey-bba1.pem, from the drop-down list. If you have obtained the private key file, select I acknowledge that I have obtained private key file SSHkey-bba1.pem and that without this file I will not be able to log in to my ECS. If no key pair is created, click View Key Pair to create or import keys. Then obtain the private key file.

    Configure an SSH key using either of the following two methods:

    • Create an SSH key

      After you create an SSH key, a public key and a private key are generated. The public key is stored in the system, and the private key is stored in the local ECS. When you log in to an ECS, the public and private keys are used for authentication.

    • Import an SSH key

      If you have obtained the public and private keys, import the public key into the system. When you log in to an ECS, the public and private keys are used for authentication.

    Table 4 Log management information

    Parameter

    Description

    Logging

    Indicates whether the tenant has enabled the log collection function.

    • : Enabled
    • : Disabled

    You can click  or  to disable or enable the log collection function, respectively.

    OBS Bucket

    Indicates the log save path, for example, s3a://mrs_log_0adca19f25834f3597602094bec12990_eu-xx.

    If an MRS cluster supporting log records fails to be created, you can use OBS to download related logs for troubleshooting.

    Procedure:

    1. Log in to the OBS management console.
    2. Select the mrs-log-<tenant_id>-<region_id> bucket from the bucket list and go to the /<cluster_id>/install_log folder to download the YYYYMMDDHHMMSS.tar.gz log, for example, /mrs_log_0adca19f25834f3597602094bec12990_eu-xx/65d0a20f-bcb7-4da3-81d3-71fef12d993d/20170818091516.tar.gz.
    Table 5 Component information

    Parameter

    Description

    Price Calculator

    Calculates the price for MRS configurations before they are ordered by the user.

    Table 6 Job configuration information

    Parameter

    Description

    Configure now

    After you click Configure now, the page for adding a tag or a bootstrap action is displayed.

    Do not configure

    You can add job configuration information later.

  5. Click Create now.

    NOTE:

    For details about the pricing, click Price Calculator.

  6. Confirm cluster specifications, and click Submit to submit a cluster creation task.
  7. Click Back to Cluster List to view the cluster status.

    For details about cluster status during cluster creation, see the Status parameter description in Table 1.

    Cluster creation takes some time. While the cluster is being created, its status is Starting. After the cluster is created successfully, the cluster status becomes Running.

    Users can create a maximum of 10 clusters at a time and manage a maximum of 100 clusters on the MRS management console.

    NOTE:

    The name of a new cluster can be the same as that of a failed or terminated cluster.

Failed to Create a Cluster

If the cluster fails to be created, the failed task automatically switches to the Manage Failed Task page. You can click  displayed in Figure 1 to access the Manage Failed Task page and move the cursor over  in the Task Status column shown in Figure 2 to view the causes. For details about how to delete the failed task, see Deleting a Failed Task.

Figure 1 Managing failed tasks
Figure 2 Causes

Table 7 provides error codes about cluster creation failure.

Table 7 Error codes

Error Code

Message

MRS.101

Insufficient quota to meet your request. Contact customer service to increase the quota.

MRS.102

The token cannot be null or invalid. Try again later or contact customer service.

MRS.103

Invalid request. Try again later or contact customer service.

MRS.104

Insufficient resources. Try again later or contact customer service.

MRS.105

Insufficient IP addresses in the existing subnet. Try again later or contact customer service.

MRS.201

Failed due to an ECS error. Try again later or contact customer service.

MRS.202

Failed due to an IAM error. Try again later or contact customer service.

MRS.203

Failed due to a VPC error. Try again later or contact customer service.

MRS.300

MRS system error. Try again later or contact customer service.