Feature/podaac 5065 (#49)

* put a try exception around running multicore, if multicore fails try single core

* fix pyling

* update changelog

* add memory calculation to determine to run single core or multicore

* fix pylint and flake8

* fix how to get a variables byte size

CHANGELOG.md

@@ -18,6 +18,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
     - Updated cmr-umm-updater to newer version and added input argument to disable association removal
   - PODAAC-4976
     - Added dateutil dependency to Jupyter-test workflow
+  - PODAAC-5065
+    - Calculate how much shared memory will be used and determine to use multicore or singlecore concise.
 ### Changed 
 ### Deprecated 
 ### Removed

podaac/merger/merge_worker.py

@@ -1,5 +1,6 @@
 """Preprocessing methods and the utilities to automagically run them in single-thread/multiprocess modes"""
 
+import math
 import multiprocessing
 from multiprocessing.shared_memory import SharedMemory
 import queue
@@ -25,6 +26,37 @@ def shared_memory_size():
         return int(default_memory_size)
 
 
+def max_var_memory(file_list, var_info, max_dims):
+    """
+    function to get the maximum shared memory that will be used for variables
+
+    Parameters
+    ----------
+    file_list : list
+        List of file paths to be processed
+    var_info : dict
+        Dictionary of variable paths and associated VariableInfo
+    """
+
+    max_var_mem = 0
+    for file in file_list:
+        with nc.Dataset(file, 'r') as origin_dataset:
+
+            for var_path, var_meta in var_info.items():
+                ds_group, var_name = resolve_group(origin_dataset, var_path)
+                ds_var = ds_group.variables.get(var_name)
+
+                if ds_var is None:
+                    target_shape = tuple(max_dims[f'/{dim}'] for dim in var_meta.dim_order)
+                    var_size = math.prod(target_shape) * var_meta.datatype.itemsize
+                    max_var_mem = max(var_size, max_var_mem)
+                else:
+                    var_size = math.prod(ds_var.shape) * var_meta.datatype.itemsize
+                    max_var_mem = max(var_size, max_var_mem)
+
+    return max_var_mem
+
+
 def run_merge(merged_dataset, file_list, var_info, max_dims, process_count, logger):
     """
     Automagically run merging in an optimized mode determined by the environment
@@ -44,15 +76,22 @@ def run_merge(merged_dataset, file_list, var_info, max_dims, process_count, logg
     """
 
     if process_count == 1:
-        _run_single_core(merged_dataset, file_list, var_info, max_dims)
+        _run_single_core(merged_dataset, file_list, var_info, max_dims, logger)
     else:
         # Merging is bottlenecked at the write process which is single threaded
         # so spinning up more than 2 processes for read/write won't scale the
         # optimization
-        _run_multi_core(merged_dataset, file_list, var_info, max_dims, 2, logger)
 
+        max_var_mem = max_var_memory(file_list, var_info, max_dims)
+        max_memory_size = round(shared_memory_size() * .95)
 
-def _run_single_core(merged_dataset, file_list, var_info, max_dims):
+        if max_var_mem < max_memory_size:
+            _run_multi_core(merged_dataset, file_list, var_info, max_dims, 2, logger)
+        else:
+            _run_single_core(merged_dataset, file_list, var_info, max_dims, logger)
+
+
+def _run_single_core(merged_dataset, file_list, var_info, max_dims, logger):
     """
     Run the variable merge in the current thread/single-core mode
 
@@ -67,6 +106,8 @@ def _run_single_core(merged_dataset, file_list, var_info, max_dims):
     max_dims : dict
         Dictionary of dimension paths and maximum dimensions found during preprocessing
     """
+
+    logger.info("Running single core ......")
     for i, file in enumerate(file_list):
         with nc.Dataset(file, 'r') as origin_dataset:
             origin_dataset.set_auto_maskandscale(False)