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
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.
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 |
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.
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 |
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
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 |
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