使用更高的AWS Glue作业执行器类型，增加任务作业的任务空间和计算资源，或者通过优化代码来减少内存占用。下面是一个使用更高的AWS Glue作业执行器类型的代码示例：

import boto3
import sys

job_name = 'my-glue-job'
job_command = {
    'Name': 'glueetl',
    'ScriptLocation': 's3://my-bucket/glue-etl-script.py',
    'PythonVersion': '3'
}
job_execution_role = 'my-glue-job-execution-role'
allocated_capacity = 10 # 10 units
max_capacity = 20 # 20 units

glue = boto3.client('glue')

# Get the default Glue ETL job runner settings
response = glue.get_job_runners()

# Get the highest available Glue ETL job runner type
max_worker_type = response['MaxWorkers']['Value']

# Create a new Glue ETL job, using the highest available worker type
response = glue.create_job(
    Name=job_name,
    Command=job_command,
    Role=job_execution_role,
    AllocatedCapacity=allocated_capacity,
    MaxCapacity=max_capacity,
    WorkerType=max_worker_type
)

job_run_id = response['JobRunId']

# Start the job
response = glue.start_job_run(
    JobName=job_name,
    JobRunId=job_run_id,
)

# Wait for the job to finish
waiter = glue.get_waiter('job_run_complete')
waiter.wait(
    JobName=job_name,
    JobRunId=job_run_id,
)

# Check the job status
response = glue.get_job_run(
    JobName=job_name,
    RunId=job_run_id,
)

status = response['JobRun']['JobRunState']

if status == 'SUCCEEDED':
    print('Glue job succeeded!')
else:
    print('Glue job failed! Status: ', status)
    sys.exit(1)