Elastic Compute Service:Big data instance types

Big data instances have the following benefits:

• Enterprise-level computing power ensures efficient and stable data processing.

• Network performance is enhanced with higher maximum internal bandwidth per instance and higher maximum packet forwarding rates to satisfy data transfer demands such as shuffling in Hadoop MapReduce at peak times.

When you use big data instances, take note of the following items:

• Instances equipped with local SSDs do not support instance configuration changes.

• Local disks can be tied only to specific instance types. The number and capacity of local disks attached to an instance vary based on the instance type. You cannot separately purchase local disks, or detach local disks from instances and then attach the disks to other instances.

• You cannot create snapshots for local disks. If you want to create an image from the system disk and data disks of an instance equipped with local SSDs, we recommend that you create an image by combining the snapshots of both the system disk and data disks. In this case, the data disks must be cloud disks.

• You cannot create images that consist of system disk snapshots and data disk snapshots based on instances equipped with local SSDs.

• You can attach a standard SSD to an instance equipped with local SSDs and extend the capacity of the standard SSD.

• Operations on an instance that are equipped with local SSDs may affect the data stored on the local SSDs. For more information, see the Impacts of instance operations on data stored on local disks section of the "Local disks" topic.

The first time you mount a file system such as ext4, you must initialize the inode table. By default, the lazyinit feature is enabled in Linux kernel v2.6.37 and later, which causes the inode table not to be initialized until file systems are mounted. In addition, local disks consume a large amount of throughput when they are being initialized, such as 600 MB/s for 30 local disks. This may affect service stability. The concurrent number of objects in lazy initialization in Linux kernel v4.x is increased to resolve this issue. For more information, see index: kernel/git/stable/linux.git. We recommend that you use the following best practices for initializing the inode table at your earliest opportunity:

1. Obtain a list of all local serial advanced technology attachment (SATA) HDDs.

2. Run the following command to initialize each local disk separately.

   In this example, an ext4 file system is created on a local disk whose device name is /dev/vdb.

   mkfs.ext4 -E lazy_itable_init=0,lazy_journal_init=0 /dev/vdb &

3. After all local disks are initialized, run the iostat -x 5 command until the I/O activities of all local disks are displayed as 0.

4. Batch run the mount command.

Features:

• This instance family is equipped with 12-TB, large-capacity, high-throughput local SATA HDDs and can provide a maximum network bandwidth of 64 Gbit/s between instances.

• Supported scenarios:

  • Big data computing and storage business scenarios in which services such as Hadoop MapReduce, HDFS, Hive, and HBase are used

  • Machine learning scenarios such as Spark in-memory computing and MLlib

  • Search and log data processing scenarios in which solutions such as Elasticsearch and Kafka are used

• This instance family supports online replacement and hot swapping of damaged disks to prevent instance shutdown.

  If a local disk fails, you receive a system event. You can handle the system event by initiating the process of repairing the damaged disk. For more information, see O&M scenarios and system events for instances equipped with local disks.

  Important

  After you initiate the process of repairing the damaged disk, data stored on the damaged disk cannot be restored.

• Compute:

  • Uses 2.7 GHz Intel^® Xeon^® Scalable (Ice Lake) processors that deliver an all-core turbo frequency of 3.5 GHz to provide consistent computing performance.

• Storage:

  • Is an instance family in which all instances are I/O optimized.

  • Supports only ESSDs and ESSD AutoPL disks.

• Network:

  • Supports IPv4 and IPv6. For information about IPv6 communication, see IPv6 communication.

  • Provides high network performance based on large computing capacity.

d3s instance types

Features:

• This instance family is equipped with high-capacity and high-throughput local disks and can provide a maximum bandwidth of 40 Gbit/s between instances.

• Supported scenarios:

  • Big data computing and storage business scenarios in which services such as Hadoop MapReduce, HDFS, Hive, and HBase are used

  • Scenarios in which EMR JindoFS and Object Storage Service (OSS) are used in combination to separately store hot and cold data and decouple storage from computing

  • Machine learning scenarios such as Spark in-memory computing and MLlib

  • Search and log data processing scenarios in which solutions such as Elasticsearch and Kafka are used

• This instance family supports online replacement and hot swapping of damaged disks to prevent instance shutdown.

  If a local disk fails, you receive a system event. You can handle the system event by initiating the process of repairing the damaged disk. For more information, see O&M scenarios and system events for instances equipped with local disks.

  Important

  After you initiate the process of repairing the damaged disk, data stored on the damaged disk cannot be restored.

• Compute:

  • Uses third-generation 2.9 GHz Intel^® Xeon^® Scalable (Ice Lake) processors that deliver an all-core turbo frequency of 3.5 GHz to provide consistent computing performance.

• Storage:

  • Is an instance family in which all instances are I/O optimized.

  • Supports only ESSDs and ESSD AutoPL disks.

• Network:

  • Supports IPv4 and IPv6. For information about IPv6 communication, see IPv6 communication.

  • Provides high network performance based on large computing capacity.

d3c instance types

Features:

• This instance family is equipped with high-capacity and high-throughput local SATA HDDs and can provide a maximum bandwidth of 35 Gbit/s between instances.

• Supported scenarios:

  • Big data computing and storage business scenarios in which services such as Hadoop MapReduce, HDFS, Hive, and HBase are used

  • Scenarios in which EMR JindoFS and OSS are used in combination to separately store hot and cold data and decouple storage from computing

  • Machine learning scenarios such as Spark in-memory computing and MLlib

  • Search and log data processing scenarios in which solutions such as Elasticsearch and Kafka are used

• This instance family supports online replacement and hot swapping of damaged disks to prevent instance shutdown.

  If a local disk fails, you receive a system event. You can handle the system event by initiating the process of repairing the damaged disk. For more information, see O&M scenarios and system events for instances equipped with local disks.

  Important

  After you initiate the process of repairing the damaged disk, data stored on the damaged disk cannot be restored.

• Compute:

  • Uses 2.5 GHz Intel^® Xeon^® Platinum 8269CY (Cascade Lake) processors.

• Storage:

  • Is an instance family in which all instances are I/O optimized.

  • Supports enhanced SSDs (ESSDs), ESSD AutoPL disks, standard SSDs, and ultra disks.

• Network:

  • Supports IPv4 and IPv6. For information about IPv6 communication, see IPv6 communication.

  • Provides high network performance based on large computing capacity.

d2c instance types

Features:

• This instance family is equipped with high-capacity and high-throughput local SATA HDDs and can provide a maximum bandwidth of 35 Gbit/s between instances.

• Supported scenarios:

  • Big data computing and storage business scenarios in which services such as Hadoop MapReduce, HDFS, Hive, and HBase are used

  • Machine learning scenarios such as Spark in-memory computing and MLlib

  • Search and log data processing scenarios in which solutions such as Elasticsearch and Kafka are used

• This instance family supports online replacement and hot swapping of damaged disks to prevent instance shutdown.

  If a local disk fails, you receive a system event. You can handle the system event by initiating the process of repairing the damaged disk. For more information, see O&M scenarios and system events for instances equipped with local disks.

  Important

  After you initiate the process of repairing the damaged disk, data stored on the damaged disk cannot be restored.

• Compute:

  • Uses 2.5 GHz Intel^® Xeon^® Platinum 8163 (Skylake) processors.

• Storage:

  • Is an instance family in which all instances are I/O optimized.

  • Supports ESSDs, ESSD AutoPL disks, standard SSDs, and ultra disks.

• Network:

  • Supports IPv4 and IPv6. For information about IPv6 communication, see IPv6 communication.

  • Provides high network performance based on large computing capacity.

d2s instance types

Features:

• This instance family is equipped with high-capacity and high-throughput local SATA HDDs and can provide a maximum bandwidth of 35 Gbit/s between instances.

• Supported scenarios:

  • Scenarios in which services such as Hadoop MapReduce, HDFS, Hive, and HBase are used

  • Machine learning scenarios such as Spark in-memory computing and MLlib

  • Search and log data processing scenarios in which solutions such as Elasticsearch are used

• Compute:

  • Offers a CPU-to-memory ratio of 1:4, which is designed for big data scenarios.

  • Uses 2.5 GHz Intel^® Xeon^® E5-2682 v4 (Broadwell) processors.

• Storage:

  • Is an instance family in which all instances are I/O optimized.

  • Supports only standard SSDs and ultra disks.

• Network:

  • Supports IPv4 and IPv6. For information about IPv6 communication, see IPv6 communication.

  • Provides high network performance based on large computing capacity.

d1ne instance types

Features:

• This instance family is equipped with high-capacity and high-throughput local SATA HDDs and can provide a maximum bandwidth of 17 Gbit/s between instances.

• Supported scenarios:

  • Scenarios in which services such as Hadoop MapReduce, HDFS, Hive, and HBase are used

  • Machine learning scenarios such as Spark in-memory computing and MLlib

  • Scenarios in which customers in industries such as Internet and finance need to compute, store, and analyze big data

  • Search and log data processing scenarios in which solutions such as Elasticsearch are used

• Compute:

  • Offers a CPU-to-memory ratio of 1:4, which is designed for big data scenarios.

  • Uses 2.5 GHz Intel^® Xeon^® E5-2682 v4 (Broadwell) processors.

• Storage:

  • Is an instance family in which all instances are I/O optimized.

  • Supports standard SSDs and ultra disks.

• Network:

  • Supports IPv4

  • Provides high network performance based on large computing capacity.

d1 instance types