Data Warehouse Flavors

GaussDB(DWS) provides standard and hybrid 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