Help Center> GaussDB(DWS)> Service Overview> Data Warehouse Flavors
Updated on 2023-12-25 GMT+08:00

Data Warehouse Flavors

GaussDB(DWS) provides standard, hybrid, and stream data warehouses. The hybrid data warehouse supports the standalone deployment. For details about the differences between them, see Data Warehouse Types.

Standard Data Warehouse (DWS 2.0) Flavors

  • A standard data warehouse (DWS 2.0) using cloud disks can be elastically scaled, providing unlimited computing and storage capacity. For details, see Table 1.
  • A standard data warehouse (DWS 2.0) using local disks cannot be scaled up. You can only increase capacity by adding nodes. For details, see Table 2.

    Step indicates the interval for increasing or decreasing the disk size during cluster configuration change. You need to select a value based on the storage step of the corresponding flavor.

    Table 1 Cloud disk flavors of a standard data warehouse (DWS 2.0)

    Flavor

    CPU Architecture

    vCPU

    Memory (GB)

    Storage Capacity Per Node

    Default Storage

    Step (GB)

    Recommended Storage

    DN number

    Scenario

    dwsx2.xlarge.m7

    x86

    4

    32

    20GB ~ 2000GB

    100

    10

    800

    1

    Suitable for GaussDB(DWS) starters. These flavors can be used for testing, learning environments, or small-scale analytics systems.

    dwsk2.xlarge

    ARM

    4

    32

    20GB ~ 2000GB

    100

    10

    800

    1

    dwsx2.xlarge.m7n

    x86

    4

    32

    20GB ~ 2000GB

    100

    10

    800

    1

    dwsx2.2xlarge.m7

    x86

    8

    64

    100 GB – 4000 GB

    200

    100

    1600

    1

    Suitable for internal data warehousing and report analysis in small- and medium-sized enterprises (SMEs).

    dwsk2.2xlarge

    ARM

    8

    64

    100 GB – 4000 GB

    200

    100

    1600

    1

    dwsx2.2xlarge.m7n

    x86

    8

    64

    100 GB – 4000 GB

    200

    100

    1600

    1

    dwsx2.4xlarge.m7

    x86

    16

    128

    100GB ~ 8000GB

    400

    100

    3200

    1

    dwsk2.4xlarge

    ARM

    16

    128

    100GB ~ 8000GB

    400

    100

    3200

    1

    dwsx2.8xlarge.m7

    x86

    32

    256

    100GB ~ 16000GB

    800

    100

    6400

    2

    Recommended for the production environment. These flavors are applicable to OLAP systems that have to deal with large data volumes, BI reports, and data visualizations on large screens for most companies.

    dwsk2.8xlarge

    ARM

    32

    256

    100GB ~ 16000GB

    800

    100

    6400

    2

    dwsx2.8xlarge.m7n

    x86

    32

    256

    100GB ~ 16000GB

    800

    100

    6400

    2

    dwsk2.12xlarge

    ARM

    48

    384

    100 GB – 24,000 GB

    1200

    100

    9600

    4

    These flavors can deliver excellent performance and are applicable to high-throughput data warehouse processing and high-concurrency online query.

    dwsx2.16xlarge.m7

    x86

    64

    512

    100 GB – 32,000 GB

    1600

    100

    12800

    4

    dwsx2.16xlarge.m7n

    x86

    64

    512

    100 GB – 32,000 GB

    1600

    100

    12800

    4

Table 2 Local disk flavors of a standard data warehouse (DWS 2.0)

Flavor

CPU Architecture

vCPU

Memory (GB)

Storage Capacity Per Node

DN number

Scenario

dws2.olap.4xlarge.i3

x86

16

128

1490GB

1

Recommended for the production environment. These flavors are applicable to OLAP systems that have to deal with large data volumes, BI reports, and data visualizations on large screens for most companies.

These flavors can deliver excellent performance and are applicable to high-throughput data warehouse processing and high-concurrency online query.

dws2.olap.4xlarge.ki1

Arm

16

64

2980GB

1

dws2.olap.8xlarge.i3

x86

32

256

2980GB

2

dws2.olap.8xlarge.ki1

Arm

32

128

5960GB

2

dws2.olap.16xlarge.i3

x86

64

512

5960GB

4

dws2.olap.16xlarge.ki1

Arm

64

228

11921GB

4

Standard Data Warehouse (DWS 3.0) Flavors

  • A standard data warehouse (DWS 3.0) using cloud disks can be elastically scaled, providing unlimited computing and storage capacity. For details, see Table 3.
  • A standard data warehouse (DWS 3.0) using cloud disks have fixed flavors. You can only expand it by adding nodes. For details about the flavors, see Table 4.
Table 3 Standard data warehouse (DWS 3.0) flavors

Flavor

CPU Architecture

vCPU

Memory (GB)

Storage Capacity Per Node

Step (GB)

DN number

Scenario

dwsx3.4U16G.4DPU

x86

4

16

20GB~2000GB

10

1

Suitable for GaussDB(DWS) starters. These flavors can be used for testing, learning environments, or small-scale analytics systems.

dwsk3.4U16G.4DPU

Arm

4

16

20GB~2000GB

10

1

dwsx3.8U32G.8DPU

x86

8

32

100GB~4000GB

100

1

Suitable for internal data warehousing and report analysis in small- and medium-sized enterprises (SMEs).

dwsk3.8U32G.8DPU

Arm

8

32

100GB~4000GB

100

1

dwsx3.16U64G.16DPU

x86

16

64

100GB~8000GB

100

1

Recommended for the production environment. These flavors are applicable to OLAP systems that have to deal with large data volumes, BI reports, and data visualizations on large screens for most companies.

dwsk3.16U64G.16DPU

Arm

16

64

100GB~8000GB

100

1

dwsx3.32U128G.32DPU

x86

32

128

100GB~16000GB

100

2

dwsk3.32U128G.32DPU

Arm

32

128

100GB~16000GB

100

2

dwsk3.48U192G.48DPU

Arm

48

192

200GB~24000GB

100

4

These flavors can deliver excellent performance and are applicable to high-throughput data warehouse processing and high-concurrency online query.

dwsx3.64U256G.64DPU

x86

64

256

200GB~32000GB

100

4

Table 4 Local disk flavors of a standard data warehouse (DWS 3.0)

Flavor

CPU Architecture

vCPU

Memory (GB)

Storage Capacity Per Node

DN number

Scenario

dws3.16U128G.i7.16DPU

x86

16

128

2980GB

1

Recommended for the production environment. These flavors are applicable to OLAP systems that have to deal with large data volumes, BI reports, and data visualizations on large screens for most companies.

These flavors can deliver excellent performance and are applicable to high-throughput data warehouse processing and high-concurrency online query.

dws3.16U64G.ki1.16DPU

ARM

16

64

5960GB

1

dws3.32U256G.i7.32DPU

x86

32

256

5960GB

2

dws3.32U128G.ki1.32DPU

ARM

32

128

11920GB

2

dws3.64U512G.i7.64DPU

x86

64

512

11920GB

4

dws3.64U228G.ki1.64DPU

ARM

64

228

23840GB

4

Hybrid Data Warehouse Flavors

  • A hybrid data warehouse can be deployed in cluster or standalone mode.
    • Cluster deployment: If the name of the selected node flavor contains h, the hybrid data warehouse can be deployed in cluster mode. You can deploy multiple nodes, scale nodes, and manage resource pools. For more information, see Table 5.
    • Standalone deployment: If the name of the selected node flavor contains h1, the hybrid data warehouse only supports standalone deployment, which does not provide HA capabilities. The storage cost can be reduced by half. A standalone data warehouse can be restored by the automatic reconstruction of ECS, and its data reliability is ensured by the EVS multi-copy mechanism. For more information, see Table 6. It is less expensive than other flavors and is a good choice for lightweight services.
Table 5 Hybrid data warehouse (cluster) flavors

Flavor

CPU Architecture

vCPU

Memory (GB)

Storage Capacity Per Node

Step (GB)

DN number

Scenario

dwsx2.h.xlarge.4.c7

x86

4

16

20GB ~ 2000GB

20

1

Suitable for GaussDB(DWS) starters. These flavors can be used for testing, learning environments, or small-scale analytics systems.

dwsk2.h.xlarge.4.kc1

Arm

4

16

20GB ~ 2000GB

20

1

dwsx2.h.xlarge.4.c7n

x86

4

16

20GB ~ 2000GB

20

1

dwsx2.h.2xlarge.4.c6

x86

8

32

100 GB – 4000 GB

100

1

Suitable for internal data warehousing and report analysis in small- and medium-sized enterprises (SMEs).

dwsx2.h.2xlarge.4.c7

x86

8

32

100 GB – 4000 GB

100

1

dwsk2.h.2xlarge.4.kc1

Arm

8

32

100 GB – 4000 GB

100

1

dwsx2.h.2xlarge.4.c7n

x86

8

32

100 GB – 4000 GB

100

1

dwsx2.h.4xlarge.4.c7

x86

16

64

100GB ~ 8000GB

100

1

Recommended for the production environment. These flavors are applicable to OLAP systems that have to deal with large data volumes, BI reports, and data visualizations on large screens for most companies.

dwsk2.h.4xlarge.4.kc1

Arm

16

64

100GB ~ 8000GB

100

1

dwsx2.h.4xlarge.4.c7

x86

16

64

100GB ~ 8000GB

100

1

dwsx2.h.8xlarge.4.c7

x86

32

128

100GB ~ 16000GB

100

2

dwsk2.h.8xlarge.4.kc1

Arm

32

128

100GB ~ 16000GB

100

2

dwsx2.h.8xlarge.4.c7n

x86

32

128

100GB ~ 16000GB

100

2

dwsk2.h.12xlarge.4.kc1

Arm

48

192

100 GB – 24,000 GB

100

4

These flavors can deliver excellent performance and are applicable to high-throughput data warehouse processing and high-concurrency online query.

dwsx2.h.16xlarge.4.c7

x86

64

256

100 GB – 32,000 GB

100

4

dwsx2.h.16xlarge.4.c7n

x86

64

256

100 GB – 32,000 GB

100

4

Table 6 Hybrid data warehouse (standalone) flavors

Flavor

CPU Architecture

vCPU

Memory (GB)

Storage Capacity Per Node

Step (GB)

DN number

Scenario

dwsx2.h1.xlarge.2.c7

x86

4

8

20GB ~ 2000GB

20

1

Suitable for GaussDB(DWS) starters. These flavors can be used for testing, learning environments, or small-scale analytics systems.

dwsk2.h1.xlarge.2.kc1

Arm

4

8

20GB ~ 2000GB

20

1

dwsx2.h1.xlarge.2.c7n

x86

4

8

20GB ~ 2000GB

20

1

dwsx2.h1.2xlarge.4.c7

x86

8

32

100 GB – 4000 GB

100

1

Suitable for internal data warehousing and report analysis in small- and medium-sized enterprises (SMEs).

dwsk2.h1.2xlarge.4.kc1

Arm

8

32

100 GB – 4000 GB

100

1

dwsx2.h1.2xlarge.4.c7n

x86

8

32

100 GB – 4000 GB

100

1

dwsx2.h1.4xlarge.4.c7

x86

16

64

100GB ~ 8000GB

100

1

Recommended for the production environment. These flavors are applicable to OLAP systems that have to deal with large data volumes, BI reports, and data visualizations on large screens for most companies.

dwsk2.h1.4xlarge.4.kc1

Arm

16

64

100GB ~ 8000GB

100

1

dwsx2.h1.4xlarge.4.c7n

x86

16

64

100GB ~ 8000GB

100

1

dwsx2.h1.8xlarge.4.c7

x86

32

128

100GB ~ 16000GB

100

2

dwsk2.h1.8xlarge.4.kc1

Arm

32

128

100GB ~ 16000GB

100

2

dwsx2.h1.8xlarge.4.c7n

x86

32

128

100GB ~ 16000GB

100

2

dwsk2.h1.12xlarge.4.kc1

Arm

48

192

100 GB – 24,000 GB

100

4

These flavors can deliver excellent performance and are applicable to high-throughput data warehouse processing and high-concurrency online query.

dwsx2.h1.16xlarge.4.c7

x86

64

256

100 GB – 32,000 GB

100

4

dwsx2.h1.16xlarge.4.c7n

x86

64

256

100GB~32000GB

100

4

Stream Data Warehouse Flavors

Table 7 Stream data warehouse flavors

Flavor

CPU Architecture

vCPU

Memory (GB)

Storage Capacity Per Node

Step (GB)

DN number

Scenario

dwsx2.rt.xlarge.m7

x86

4

32

20GB ~ 2000GB

20

1

Suitable for GaussDB(DWS) starters. These flavors can be used for testing, learning environments, or small-scale analytics systems.

dwsk2.rt.xlarge.km1

ARM

4

32

20GB ~ 2000GB

20

1

dwsx2.rt.xlarge.m7n

x86

4

32

20GB ~ 2000GB

20

1

dwsx2.rt.2xlarge.m7

x86

8

64

100 GB – 4000 GB

100

1

Suitable for internal data warehousing and report analysis in SMEs.

dwsk2.rt.2xlarge.km1

Arm

8

64

100 GB – 4000 GB

100

1

dwsx2.rt.2xlarge.m7n

x86

8

64

100 GB – 4000 GB

100

1

dwsx2.rt.4xlarge.m7

x86

16

128

200GB ~ 8000GB

100

1

dwsk2.rt.4xlarge.km1

ARM

16

128

200GB ~ 8000GB

100

1

dwsx2.rt.8xlarge.m7

x86

32

256

100GB ~ 16000GB

100

2

Recommended for the production environment. These flavors are applicable to OLAP systems that have to deal with large data volumes, BI reports, and data visualizations on large screens for most companies.

dwsk2.rt.8xlarge.km1

Arm

32

256

100GB ~ 16000GB

100

2

dwsx2.rt.8xlarge.m7n

x86

32

256

100GB ~ 16000GB

100

2

dwsk2.rt.12xlarge.km1

Arm

48

384

100 GB – 24,000 GB

100

4

These flavors can deliver excellent performance and are applicable to high-throughput data warehouse processing and high-concurrency online query.

dwsx2.rt.16xlarge.m7

x86

64

512

100 GB – 32,000 GB

100

4

dwsx2.rt.16xlarge.m7n

x86

64

512

100 GB – 32,000 GB

100

4