[docs] job execution (#7776)

docs/content/concepts/ops-jobs-graphs/job-execution.mdx

@@ -148,3 +148,46 @@ src="/images/concepts/solids-pipelines/solid-selection.png"
 width={3808}
 height={2414}
 />
+
+## Controlling Job Execution
+
+Each <PyObject object="JobDefinition" /> contains an <PyObject object="ExecutorDefinition" /> that determines how it will be executed.
+
+This `executor_def` property can be set to allow for different types of isolation and parallelism, ranging from executing all the ops in the same process to executing each op in its own Kubernetes pod. See [Executors](/deployment/executors) for more details.
+
+### Default Job Executor
+
+The default job executor definition defaults to multiprocess execution. It also allows you to toggle between in process and multiprocess execution via config.
+
+Here is an example of run config as yaml you could provide in the dagit playground to do an in process execution.
+
+```python file=/concepts/solids_pipelines/pipeline_execution.py startafter=start_ip_yaml endbefore=end_ip_yaml
+execution:
+  config:
+    in_process:
+```
+
+Additional config options are available for multiprocess execution that can help with performance. This includes limiting the max concurrent subprocesses and controlling how those subprocess are spawned.
+
+The example below sets the run config directly on the job to explicitly set the max concurrent subprocesses to 4, and change the subprocess start method to use a forkserver.
+
+```python file=/concepts/solids_pipelines/pipeline_execution.py startafter=start_mp_cfg endbefore=end_mp_cfg
+@job(
+    config={
+        "execution": {
+            "config": {
+                "multiprocess": {
+                    "start_method": {
+                        "forkserver": {},
+                    },
+                    "max_concurrent": 4,
+                },
+            }
+        }
+    }
+)
+def forkserver_job():
+    multi_three(add_two(return_one()))
+```
+
+Using forkserver is a great way to reduce per process overhead during multiprocess execution, but can cause issues with certain libraries. More details can be found [here](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods).

examples/docs_snippets/docs_snippets/concepts/solids_pipelines/pipeline_execution.py

@@ -43,3 +43,35 @@ def execute_subset():
 @op
 def total(in_1: int, in_2: int, in_3: int, in_4: int):
     return in_1 + in_2 + in_3 + in_4
+
+
+ip_yaml = """
+# start_ip_yaml
+
+execution:
+  config:
+    in_process:
+
+# end_ip_yaml
+"""
+
+# start_mp_cfg
+@job(
+    config={
+        "execution": {
+            "config": {
+                "multiprocess": {
+                    "start_method": {
+                        "forkserver": {},
+                    },
+                    "max_concurrent": 4,
+                },
+            }
+        }
+    }
+)
+def forkserver_job():
+    multi_three(add_two(return_one()))
+
+
+# end_mp_cfg

examples/docs_snippets/docs_snippets_tests/concepts_tests/solids_pipelines_tests/test_pipeline_execution.py

@@ -1,5 +1,10 @@
+import yaml
+
+from dagster import execute_pipeline
 from docs_snippets.concepts.solids_pipelines.pipeline_execution import (
     execute_subset,
+    forkserver_job,
+    ip_yaml,
     my_job,
 )
 
@@ -11,3 +16,11 @@ def test_execute_my_job():
 
 def test_solid_selection():
     execute_subset()
+
+
+def test_yaml():
+    execute_pipeline(my_job, run_config=yaml.safe_load(ip_yaml))
+
+
+def test_forkserver():
+    assert forkserver_job  # just assert definition created