Understanding application behavior
Default application behavior
Auto-start — An application by default is configured to auto-start on job submission. You can turn this feature off.
Auto-stop — An application by default is configured to
auto-stop when idle for 15 minutes. When an application changes to the
STOPPED state, it releases any configured pre-initialized capacity. You can
modify the amount of idle time before an application auto-stops, or you can turn this
feature off.
Maximum capacity
You can configure the maximum capacity that an application can scale up to. You can specify your maximum capacity in terms of CPU, memory (GB), and disk (GB).
Note
We recommend configuring your maximum capacity to be proportional to your supported worker sizes by multiplying the number of workers by their sizes. For example, if you want to limit your application to 50 workers with 2 vCPUs, 16 GB for memory, and 20 GB for disk, set your maximum capacity to 100 vCPUs, 800 GB for memory, and 1000 GB for disk.
Supported worker configurations
The following table shows supported worker configurations and sizes that you can specify for EMR Serverless. You can configure different sizes for drivers and executors based on the need of your workload.
| CPU | Memory | Default ephemeral storage |
|---|---|---|
|
1 vCPU |
Minimum 2 GB, maximum 8 GB, in 1 GB increments |
20 GB - 200 GB |
|
2 vCPU |
Minimum 4 GB, maximum 16 GB, in 1 GB increments |
20 GB - 200 GB |
|
4 vCPU |
Minimum 8 GB, maximum 30 GB, in 1 GB increments |
20 GB - 200 GB |
|
8 vCPU |
Minimum 16 GB, maximum 60 GB, in 4 GB increments |
20 GB - 200 GB |
|
16 vCPU |
Minimum 32 GB, maximum 120 GB, in 8 GB increments |
20 GB - 200 GB |
CPU — Each worker can have 1, 2, 4, 8, or 16 vCPUs.
Memory — Each worker has memory, specified in GB, within the
limits listed in the earlier table. Spark jobs have a memory overhead, meaning that the
memory they use is more than the specified container sizes. This overhead is specified with
the properties spark.driver.memoryOverhead and
spark.executor.memoryOverhead. The overhead has a default value of 10% of
container memory, with a minimum of 384 MB. You should consider this overhead when you
choose worker sizes.
For example, If you choose 4 vCPUs for your worker instance, and a pre-initialized storage capacity of 30 GB, then you should set a value of approximately 27 GB as executor memory for your Spark job. This maximizes the utilization of your pre-initialized capacity. Usable memory would be 27 GB, plus 10% of 27 GB (2.7 GB), for a total of 29.7 GB.
Disk — You can configure each worker with temporary storage disks with a minimum size of 20 GB and a maximum of 200 GB. You only pay for additional storage beyond 20 GB that you configure per worker.
