Scaling Up Sorted Numbering Workflows Beyond The Arcmap Gui

The ArcMap graphical user interface provides intuitive tools for sorting and numbering features based on user-defined criteria. However, the in-memory processing and single-threaded execution impose limits on the size of datasets that can be efficiently manipulated. As feature class sizes grow into the millions of records, manual workflows become impractical.

By leveraging Python and ArcPy, we can automate the repetitive tasks of sorting and numbering records while taking advantage of more scalable approaches. Utilizing data cursors and partitions, instituting batch-style processing, and running operations in parallel threads can facilitate smooth numbering across arbitrarily large datasets.

Automating Workflows with Python and ArcPy

Python provides excellent built-in and third-party modules for text processing, data analysis, and automation. Combined with ArcPy site-package to access and manipulate spatial data, we obtain a flexible framework for ETL and geoprocessing scripts.

The arcpy.da data access cursors enable efficient row-by-row iteration across table records. Update cursors allow modifying attribute values while tracking changes. Dictionary types offer fast lookups for tracking last used numbers. With some creative scripting, we can build reusable functions and classes to automate sorting and numbering procedures.

Example Code for Sorting and Numbering Features in Batches

The following sample code defines a SortAndNumber class with methods that encapsulate the sequential steps:

import arcpy

class SortAndNumber:

    def __init__(self, in_table):
        self.in_table = in_table  
        self.mapping_dict = {}

    def sort_table(self, sort_field):
        # Use arcpy.da.Sort to efficiently sort table by key field
        arcpy.Sort_management(in_table, out_table, [[sort_field, "ASCENDING"]])

    def assign_numbers(self, value_field):
        # Use update cursor to increment and assign numbers
        with arcpy.da.UpdateCursor(self.in_table, [sort_field, value_field]) as cursor:
            for row in cursor:
                 # Logic to increment number and update row value

    def validate_numbering(self):
        # Checks that values correctly sequence 
        with arcpy.da.SearchCursor(self.in_table, [sort_field, value_field]) as cursor:
            # Compare current and previous values

# Example usage:

table = r'C:\project\data.gdb\parcels' 
sorter = SortAndNumber(table)


By encapsulating key logic in reusable Python classes and functions backed by data access cursors, we lay the groundwork for scalable sort and numbering processes.

Leveraging Cursors and Update Cursors for Performance

The arcpy.da module provides specialized cursor types to efficiently navigate table data:

  • SearchCursor – Read-only forward iteration over rows
  • UpdateCursor – Read/write access enabling field updates
  • InsertCursor – Adds new records during iteration

Unlike row-by-row RecordSet objects, cursors avoid loading entire tables contents into memory. They also support targeted queries via SQL syntax and spatial properties to restrict rows returned.

For large tables, fetching only the key fields needed for sorting, numbering and validation ensures snappy performance. Explicitly closing cursors after usage releases database resources sooner.

Let’s augment our previous example with some cursor best practices:

def optimized_assign_numbers(self, value_field):
    # Only required fields queried, minimizing rows returned
    field_list = [self.sort_field, value_field]  
    sql_clause = f"{value_field} IS NULL" # Only null values updated  

    # Update cursor logic...

    with arcpy.da.UpdateCursor(self.in_table, field_list, sql_clause) as cursor:
        for row in cursor:
            # Increment and update number

def high_performance_validation(self):
    # Read-only forward iteration of minimum fields
    fields = [self.sort_field, self.value_field]

    with arcpy.da.SearchCursor(self.in_table, fields) as cursor:
        previous = None
        for row in cursor:
            # Compare current and previous

Profile cursor workflows to confirm lean data transfer between script and database. This keeps processing time focused on value-added numbering logic.

Using Python Dictionaries to Track Last Numbers Assigned

A persistent challenge when numbering records across batches is avoiding duplicate or skipped values if workflows restart. By leveraging Python dictionary types with fast key-based lookup times, we can elegantly track the last used number for each sorting group across script executions.

class SortAndNumber:
    def __init__(self):
         self.next_numbers = {} # Key = sort group, Value = counter  

    def get_next_number(self, sort_group):
        next_num = self.next_numbers.get(sort_group, 1)
        self.next_numbers[sort_group] = next_num + 1
        return next_num

    def persist_numbering(self):
        # Serialize self.next_numbers to JSON on disk

The simplest approach is serializing the entire dictionary to disk as script ends, then deserializing upon restart to populate initial numbering state. More advanced usage could utilize database tables to provide persistence.

Partitioning Data Spatially for Parallel Processing

Applying sort and number sequences across database partitions enables straightforward parallelization. Spatial boundaries provide ideal partitions across geometry-driven records. Python’s multiprocessing module scales out numbering batches efficiently.

Key steps:

  1. Define spatial grid dividing dataset footprint into n equal regions
  2. Intersect grid regions with feature class to generate partitions
  3. Serialize last numbers per region to file
  4. Spin up parallel processes to handle 1 region per thread
  5. Merge, validate, and report on results

Used judiciously, partitioning circumvents desktop memory limits while minimizing complexity. Small code changes reap exponential gains.

Example Code for Multi-threaded Sort/Number Workflow

from multiprocessing import Process

def process_partition(sorter, region):
    # Deserialize last numbers
    # Fetch rows intersecting region 
    # Run sort_and_number sequence

if __name__ == '__main__':
    sorter = SortAndNumber()
    regions = split_dataset_by_grid(dataset) 

    procs = []
    for region in regions:
        proc = Process(target=process_partition,
                       args=(sorter, region))

    # Join all threads back together        
    for proc in procs:

    # Merge partition results
    # Validate numbering
    # Report run statistics

Handling Errors and Exceptions Gracefully

Robust Python scripts should anticipate and respond appropriately to anomalous conditions that may cause failures:

  • Network blips or hardware issues losing database connections
  • Cursor timeouts due to long read/write operations
  • Inconsistent data types or values causing type conversion exceptions
  • Memory errors for tables exceeding available resources

Built-in try/except blocks allow catching Exceptions at various levels of scope:

import arcpy, sys, time

    # Main script logic...

except arcpy.ExecuteError:
    # Catch geoprocessing errors

except Exception as e:
    # Logs handling for all other exception types
    print(f'Failed due to unexpected error: {e}')

    # Executes after try/except blocks
    print('Script complete')

Strategically handling errors keeps workflows running to completion even when processing irregularities appear in production systems.

Best Practices for Smooth Numbering Across Batches

For large datasets numbering many millions of records, running in discrete batches avoids memory bottlenecks. Several techniques keep numbering logically sequential across batches:

  • Assign block ranges to each partition as padding for future insertions
  • Log last maximum value per batch to inform subsequent runs
  • overlap batch spatial filters to validate anchoring
  • only update null values to reduce collision potential
  • build validation reports and visualizations at batch level

Unit and integration testing pinpoints corner cases with duplicate or out-of-sequence values. Periodic profiling during sustained runs tracks performance and results.

When used properly, batch-based processing provides scalability while controlling side effects. Balance partitioning granularity, monitoring rigor and fault tolerance for smooth sailing!

Verifying Correct Final Numbering Sequences

After completing intensive sort and number computations across sizable datasets, inspecting results to confirm expected sequences provides confidence before committing final updates. Strategies for verification include:

  • Statistical profiling – Null counts, min, max and gaps in numbering values
  • Spatial visualization – Graduated symbol maps confirming patterns
  • Automated validation – Cursor walks checking sequential differences fall within threshold
  • Manual inspection – Targeted queries to spot check key subset of records

For batch workflows, verifying smooth transitions across partition boundaries helps identify spurious breaks in sequence. Rigorously validating results provides quality assurance before propagating changes, enabling early detection and correction of systemic issues.

Additional Examples and Use Cases

Common workflow needs enabled by programmatic sorting, numbering and validation include:

  • Parcel numbering by section-township-range grids
  • Address geocoding and street side normalization
  • Point cloud classification with smoothed hierarchies
  • Topographic contour generation and elevation normalization

The general framework holds tremendous potential for adaption. Python unlocks this flexibility while handling large datasets at enterprise scales.

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