Dedicated compute node

To decouple streaming and serving, you need at least one compute node with the streaming role and one compute node with the serving role.

When launching a compute node, its role can be specified via either the --role command-line argument, or RW_COMPUTE_NODE_ROLE environment variable.

You need to restart the node to update the role. A role can be one of:

• both: The default role, if not specified. Indicates that the compute node is available for both streaming and serving.
• serving: Indicates that the compute node is read-only and executes batch queries only.
• streaming: Indicates that the compute node is only available for streaming.

For specific changes required in the YAML file, see Separate batch streaming modes.

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Configure a serving compute node for batch queries

You can use a TOML configuration file to configure a serving compute node. For detailed instructions, see Node-specific configurations.

Unlike a general-purpose both compute node, a serving compute node doesn’t require memory allocation or reservation for shared buffer and operator caches. Instead, it’s more efficient to increase the sizes of the block and meta caches. However, making these caches too large can limit the scope of data that batch queries can execute.

Here’s an example configuration for a serving compute node with 16GB of memory which you can find in /risingwave/src/config/serving.toml:

[storage]
# Shared buffer is not needed for a serving-only compute node.
shared_buffer_capacity_mb = 1

# Compactor is irrelevant to a serving-only compute node.
compactor_memory_limit_mb = 1

# Allocate 30% of total memory to block cache: 16GB * 0.3 = 4.8GB
block_cache_capacity_mb = 4800

# Allocate 10% of total memory to meta cache: 16GB * 0.1 = 1.6GB
meta_cache_capacity_mb = 1600

The remaining memory (16GB - 4.8GB - 1.6GB - reserved memory 16GB * 0.3) is used for executing serving queries. We call it “compute memory”. If a batch query is resource-intensive and its runtime memory consumption exceeds the available compute memory, it will terminate itself automatically before triggering an out-of-memory (OOM) error.

While we don’t recommend executing OLAP-style batch queries that require a large amount of input data, you can adjust the configuration if such a query is needed and the default configuration leaves too little compute memory. Feel free to allocate less memory for the block cache and meta cache to increase the compute memory.