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Table of Contents

Overview

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Table of Contents

Overview

• The Bulk Loader is a process that is used to add several new addresses to the EAS at one time.
• There are several stages that make up the Bulk Loader process, outlined below in Summary and Details.

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Running on DEV or QA first is a requirement

Required! Run addresses through DEV or QA first

Important considerations when running the Bulk Loader

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This optional stage is run once per Bulk Loader process. This stage can be skipped if the reference dataset is already available or if the optional 'filter by reference' step (Step 2.5) is skipped.

•  

  Step 1.1 - Import reference dataset

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1. input_file - The relative path to the raw CSV file.
2. output_table - The name of the table for the imported records.

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Import CSV file into PostgreSQL table (^++<odbc>)

python csv2pg.py \
  --odbc_server=<odbc-server> \
  --odbc_port=<odbc-port> \
  --odbc_database=<odbc-database> \
  --odbc_uid=<odbc-uid> \
  --odbc_pwd=<odbc-pwd> \
  --input_file=./path/to/raw_reference_file.csv \
  --output_table=reference_raw

...

1. This step generates a new table. The name of the new table is passed as a required command line argument.

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1. All columns in the input CSV file are imported as fields in the new table.
2. sfgisgapid: a new serial, not null, primary key

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1. reference_raw.csv - The input CSV table serves as the artifact for this step.

•  

  Step 1.2 - Parse reference dataset

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1. address_column: Name of address field. Required.
2. city_column: Name of city field. Optional.
3. state_column: Name of state field. Optional.
4. zip_column: Name of ZIP Code field. Optional.
5. output_table: Name of the output table. Required.

...

Parse address table (^++<odbc>)

python parse_address_table.py \
  --odbc_server=<odbc-server> \
  --odbc_port=<odbc-port> \
  --odbc_database=<odbc-database> \
  --odbc_uid=<odbc-uid> \
  --odbc_pwd=<odbc-pwd> \
  --address_table=reference_raw \
  --primary_key=sfgisgapid \
  --address_column=address \
  --city_column=city \
  --state_column=state \
  --zip_column=zip \
  --output_table=reference_parsed \
  --output_report_file=./output/reference.xlsx \
  --limit=-1

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1. This step generates a new table. The new table contains all of the fields and records of the input table with additional fields noted below.

...

1. The output consists of a field for every kind of parsed address part that is generated by the parsing tool, Parserator. It also contains additional concatenated fields as defined in the FGDC's United States Thoroughfare, Landmark, and Postal Address Data Standard as well as metadata fields.
   
   1. address_to_parse character varying(255)
   2. address_number bigint
   3. address_number_suffix character varying(255)
   4. street_name_pre_directional character varying(255)
   5. street_name_pre_type character varying(255)
   6. street_name character varying(255)
   7. street_name_post_type character varying(255)
   8. street_name_post_directional character varying(255)
   9. subaddress_type character varying(255)
   10. subaddress_identifier character varying(255)
   11. state_name character varying(255)
   12. zip_code character varying(255)
   13. complete_address_number character varying(255)
   14. complete_street_name character varying(255)
   15. complete_subaddress character varying(255)
   16. complete_place_name character varying(255)
   17. delivery_address_with_unit character varying(255)
   18. delivery_address_without_unit character varying(255)
   19. parsed_address character varying(1000)
   20. parser_had_issues boolean
   21. parser_message character varying(2000)
   22. parserator_tag character varying(255)
   23. address_number_parity character varying(255)
   24. counter integer

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1. reference_parsed.csv - Export the new table, e.g. reference_parsed, as a CSV, e.g. reference_parsed.csv, and archive in the network folder dedicated to artifacts for the Bulk Loader iteration.

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This stage is run once per Bulk Loader process. This stage involves importing, parsing and filtering the dataset. The result will be a dataset that will be geocoded in Stage 3.

•  

  Step 2.1 - Import source dataset

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Suggested folder layout

Artifacts are generated throughout the stages of the Bulk Loader Process. Below is a suggested folder and file layout for the various artifacts.

###########
# Folders #
###########

./ # root (YYYYMMDD-tag e.g. 20190510-SourceNameHere)
./gap # Artifacts from the General Address Parser stage
./geocoder # input csv and output shapefiles
./bulkload_001 # artifacts from the first batch
./bulkload_00N # artifacts from the Nth batch, if any
./excluded_records # records from all stages set aside for later review

./bulkload_00N # artifacts from the Nth batch
./bulkload_00N/shapefile/ # this is the input for the Bulk Loader step

./excluded_records # records from all stages set aside for later review

./excluded_records/gap
./excluded_records/geocoder
./excluded_records/bulkload_001
./excluded_records/bulkload_00N

#########
# Files #
#########

./gap/gap_YYYYMMDD.csv # GAP output; geocoder input

./geocoder/bulkloader.mxd # ArcMap file

./geocoder/addresses_geocoded.shp # All geocoded records
./geocoder/geocode_score_100.shp # Records with non-tied, perfect geocoder score

./bulkload_00N/shapefile/bulkload.shp # Input for the Bulk Loader stage

./bulkload_00N/address_base.csv # New base records
./bulkload_00N/addresses.csv # New base+unit records
./bulkload_00N/change_request.csv # One-record containing unique id change request id associated with this batch
./bulkload_00N/record-counts-after.csv # EAS record-count after Bulk Load
./bulkload_00N/record-counts-before.csv # EAS record-count before Bulk Load

./excluded_records/geocoder/geocode_under_100.shp # Tied and/or not perfect geocoder score

./excluded_records/bulkload_00N/address_extract_exceptions.csv
./excluded_records/bulkload_00N/address_extract_exception_totals.csv

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details details

Details for each Bulk Loader Stage

Anchor
stage1 stage1

Stage 1 - Import and parse reference dataset (Optional)

This optional stage is run once per Bulk Loader process. This stage can be skipped if the reference dataset is already available or if the optional 'filter by reference' step (Step 2.5) is skipped.

•  

  Step 1.1 - Import reference dataset

1. Script Name (*) csv2pg.py
2. URL https://bitbucket.org/sfgovdt/sfgis-general-address-parser/src/master/csv2pg.py
3. Important arguments
   1. input_file - The relative path to the raw CSV file.
   2. output_table - The name of the table for the imported records.
4. Example usage

   1. Import CSV file into PostgreSQL table (^++<odbc>)

      Code Block
      language text firstline 1 title csv2pg linenumbers true
      python csv2pg.py \ --odbc_server=<odbc-server> \ --odbc_port=<odbc-port> \ --odbc_database=<odbc-database> \ --odbc_uid=<odbc-uid> \ --odbc_pwd=<odbc-pwd> \ --input_file=./path/to/raw_reference_file.csv \ --output_table=reference_raw

      
      

5. Output table
   1. This step generates a new table. The name of the new table is passed as a required command line argument.
6. Output fields
   1. All columns in the input CSV file are imported as fields in the new table.
   2. sfgisgapid: a new serial, not null, primary key
7. Artifacts (**)
   1. reference_raw.csv - The input CSV table serves as the artifact for this step.

•  

  Step 1.2 - Parse reference dataset

1. Script Name (*) parse_address_table.py
2. URL https://bitbucket.org/sfgovdt/sfgis-general-address-parser/src/master/csv2pgparse_address_table.py
3. Important arguments
   input_file: The relative path to the raw CSV file
   1. address_column: Name of address field. Required.
   2. city_column: Name of city field. Optional.
   3. state_column: Name of state field. Optional.
   4. zip_column: Name of ZIP Code field. Optional.
   5. output_table: The name  Name of the table for the imported recordsoutput table. Required.
4. Example usage

   1. Import CSV file into PostgreSQL table Parse address table (^++<odbc>)

      Code Block
      language text firstline 1 title csv2pgparse_address_table.py linenumbers true
      python csv2pgparse_address_table.py \ --odbc_server=<odbc-server> \ --odbc_port=<odbc-port> \ --odbc_database=<odbc-database> \ --odbc_uid=<odbc-uid> \ --odbc_pwd=<odbc-pwd> \ --inputaddress_file=./path/to/raw_source_file.csvtable=reference_raw \ --primary_key=sfgisgapid \ --address_column=address \ --city_column=city \ --state_column=state \ --zip_column=zip \ --output_table=addresses_rawreference_parsed \ --output_report_file=./output/reference.xlsx \ --limit=-1

      
      

5. Output table
   1. This step generates a new table. The name of the new table is passed as a required command line argument.
6. Output fields
   1. All columns in the input CSV file are imported as fields in the new table.
   2. sfgisgapid: a new serial, not null, primary key
7. Artifacts (**)
   1. addresses_raw.csv - The input CSV table serves as the artifact for this step.

•  

  Step 2.2 - Exclude from source dataset where address_number has range

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addresses_no_range 

CREATE TABLE addresses_no_range AS SELECT * FROM addresses_raw WHERE NOT add_number LIKE '%-%';

addresses_with_range 

CREATE TABLE addresses_with_range AS SELECT * FROM addresses_raw WHERE add_number LIKE '%-%';

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1. addresses_no_range.csv - This is an archive of the records that made it past the filter to the next step.
2. addresses_with_range.csv - This is an archive of the records that did not make it past the filter. This artifact is part of the exclusion set of addresses that are set aside for later review.

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1. 1.  The new table contains all of the fields and records of the input table with additional fields noted below.

2. Anchor
   parserator_fields parserator_fields

   Output fields
   1. The output consists of a field for every kind of parsed address part that is generated by the parsing tool, Parserator. It also contains additional concatenated fields as defined in the FGDC's United States Thoroughfare, Landmark, and Postal Address Data Standard as well as metadata fields.
      
      1. address_to_parse character varying(255)
      2. address_number bigint
      3. address_number_suffix character varying(255)
      4. street_name_pre_directional character varying(255)
      5. street_name_pre_type character varying(255)
      6. street_name character varying(255)
      7. street_name_post_type character varying(255)
      8. street_name_post_directional character varying(255)
      9. subaddress_type character varying(255)
      10. subaddress_identifier character varying(255)
      11. state_name character varying(255)
      12. zip_code character varying(255)
      13. complete_address_number character varying(255)
      14. complete_street_name character varying(255)
      15. complete_subaddress character varying(255)
      16. complete_place_name character varying(255)
      17. delivery_address_with_unit character varying(255)
      18. delivery_address_without_unit character varying(255)
      19. parsed_address character varying(1000)
      20. parser_had_issues boolean
      21. parser_message character varying(2000)
      22. parserator_tag character varying(255)
      23. address_number_parity character varying(255)
      24. counter integer
3. Artifacts (**)
   1. reference_parsed.csv - Export the new table, e.g. reference_parsed, as a CSV, e.g. reference_parsed.csv, and archive in the network folder dedicated to artifacts for the Bulk Loader iteration.

Anchor
stage2 stage2

Stage 2 - Import, parse and filter source dataset

This stage is run once per Bulk Loader process. This stage involves importing, parsing and filtering the dataset. The result will be a dataset that will be geocoded in Stage 3.

•  

  Step 2.1 - Import source dataset

1. Script Name (*) parse_address_tablecsv2pg.py
2. URL https://bitbucket.org/sfgovdt/sfgis-general-address-parser/src/master/parse_address_tablecsv2pg.py
3. Important arguments
   1. addressinput_column: Name of address field. Required.
   2. city_column: Name of city field. Optional.
   3. state_column: Name of state field. Optional.
   4. zip_column: Name of ZIP Code field. Optional.
   5. output_table: Name of the output table. Required.
   Example usage
   1. Parse address table file: The relative path to the raw CSV file.
   2. output_table: The name of the table for the imported records.
4. Example usage

   1. Import CSV file into PostgreSQL table (^++<odbc>)

      Code Block
      language text firstline 1 title parse_address_tablecsv2pg linenumbers true
      python parse_address_tablecsv2pg.py \ --odbc_server=<odbc-server> \ --odbc_port=<odbc-port> \ --odbc_database=<odbc-database> \ --odbc_uid=<odbc-uid> \ --odbc_pwd=<odbc-pwd> \ --addressinput_table=addresses_no_rangefile=./path/to/raw_source_file.csv \ --primaryoutput_key=sfgisgapid \ --address_column=address \ --city_column=city \ --state_column=state \ --zip_column=zip \ --output_table=addresses_parsed \ --output_report_file=./output/source_report.xlsx \ --limit=-1

   Output table

   1. table=addresses_raw

      
      

5. Output table
   1. This step generates a new table.  The new table contains all The name of the fields and records of the input table with additional fields noted belownew table is passed as a required command line argument.
6. Output fields
   1. The output consists of a field for every kind of parsed address part that is generated by the parsing tool, Parserator. It also contains additional concatenated fields as defined in the FGDC's United States Thoroughfare, Landmark, and Postal Address Data Standard as well as metadata fields. See also Step 1.2 Output Fields.All columns in the input CSV file are imported as fields in the new table.
   2. sfgisgapid: a new serial, not null, primary key
7. Artifacts (**)
   1. addresses_parsedraw.csv: Export the new table, e.g. addresses_parsed, as a CSV, e.g. addresses_parsed.csv, and archive in the network folder dedicated to artifacts for the Bulk Loader iteration. - The input CSV table serves as the artifact for this step.

•  

  Step 2.

  4

  2 -

  Exclude addresses where street_name_post_direction is not blank and corresponding streets

• The Bulk Loader is unable to correctly import addresses that contain a post directional value. As such these addresses as well as all corresponding addresses without a post directional value must be filtered out of the dataset.
• See also Issue 168, Provide for street names that have a post directional.

...

1. output_table: The name of the table for the filtered records.

...

Remove records with post directional values  (^++<odbc>)

python remove_postdirectionals.py \
  --odbc_server=<odbc-server> \
  --odbc_port=<odbc-port> \
  --odbc_database=<odbc-database> \
  --odbc_uid=<odbc-uid> \
  --odbc_pwd=<odbc-pwd> \
  --address_table=addresses_parsed \
  --output_table=addresses_no_post_dir \
  --output_report_file=./output/source_report.xlsx \
  --limit=-1

Create table addresses_with_post_dir

CREATE TABLE addresses_with_post_dir AS SELECT * FROM addresses_parsed WHERE NOT id IN (SELECT id FROM addresses_no_post_dir);

...

•  Exclude from source dataset where address_number has range

1. Create Output Tables
   

   1. addresses_no_range 

      Code Block
      language sql firstline 1 title addresses_no_range linenumbers true
      CREATE TABLE addresses_no_range AS SELECT * FROM addresses_raw WHERE NOT add_number LIKE '%-%';

      
      

   2. addresses_with_range 

      Code Block
      language sql firstline 1 title addresses_with_range linenumbers true
      CREATE TABLE addresses_with_range AS SELECT * FROM addresses_raw WHERE add_number LIKE '%-%';

      
      

2. Artifacts (**)
   1. addresses_no_range.csv - This is an archive of the records that made it past the filter to the next step.
   2. addresses_with_range.csv - This is an archive of the records that did not make it past the filter. This artifact is part of the exclusion set of addresses that are set aside for later review.

•  

  Step 2.3 - Parse source dataset

1. Script Name (*) parse_address_table.py
2. URL https://bitbucket.org/sfgovdt/sfgis-general-address-parser/src/master/parse_address_table.py
3. Important arguments
   1. address_column: Name of address field. Required.
   2. city_column: Name of city field. Optional.
   3. state_column: Name of state field. Optional.
   4. zip_column: Name of ZIP Code field. Optional.
   5. output_table: Name of the output table. Required.
4. Example usage

   1. Parse address table (^++<odbc>)

      Code Block
      language text firstline 1 title parse_address_table linenumbers true
      python parse_address_table.py \ --odbc_server=<odbc-server> \ --odbc_port=<odbc-port> \ --odbc_database=<odbc-database> \ --odbc_uid=<odbc-uid> \ --odbc_pwd=<odbc-pwd> \ --address_table=addresses_no_range \ --primary_key=sfgisgapid \ --address_column=address \ --city_column=city \ --state_column=state \ --zip_column=zip \ --output_table=addresses_parsed \ --output_report_file=./output/source_report.xlsx \ --limit=-1

      
      

5. Output table
   1. This step generates a new table. The new table contains all of the fields and records of the input table excluding any that have a post direction value and corresponding addresses with streets without the post directionwith additional fields noted below.
6. Output fields
   1. The output consists of a field for every kind of parsed address part that is generated by the parsing tool, Parserator. It also contains additional concatenated fields as defined in the FGDC's United States Thoroughfare, Landmark, and Postal Address Data Standard as well as metadata fields. See also Step 1.2 Output Fields.
7. Artifacts (**)
   1. addresses_no_post_dir.csv - This is an archive of the records that made it past the filter to the next step.
   2. addresses_with_post_dir.csv - This is an archive of the records that did not make it past the filter. This artifact is part of the exclusion set of addresses that are set aside for later review.
   3. parsed.csv: Export the new table, e.g. addresses_parsed, as a CSV, e.g. addresses_parsed.csv, and archive in the network folder dedicated to artifacts for the Bulk Loader iteration.

•  

  Step 2.

  5

  4 - Exclude addresses

  not in reference (Optional)

 This optional step excludes from the source dataset any addresses that are not also found in the parsed reference dataset.

...

• where street_name_post_direction is not blank and corresponding streets

• The Bulk Loader is unable to correctly import addresses that contain a post directional value. As such these addresses as well as all corresponding addresses without a post directional value must be filtered out of the dataset.
• See also Issue 168, Provide for street names that have a post directional.

1. Script Name (*)  is_address_in_referenceremove_postdirectionals.py
2. URL https://bitbucket.org/sfgovdt/sfgis-general-address-parser/src/master/is_address_in_referenceremove_postdirectionals.py
3. Important arguments
   1. source_output_field - table: The name of a new integer field that will store the results of the stepthe table for the filtered records.
4. Example usage

   1. Add field to flag if address is found in reference  Remove records with post directional values  (^++<odbc>)

      Code Block
      language text firstline 1 title is_address_in_referenceremove_postdirectionals linenumbers true
      python is_address_in_referenceremove_postdirectionals.py \ --odbc_server=<odbc-server> \ --odbc_port=<odbc-port> \ --odbc_database=<odbc-database> \ --odbc_uid=<odbc-uid> \ --odbc_pwd=<odbc-pwd> \ --sourceaddress_table=addresses_no_post_dirparsed \ --referenceoutput_table=reference_parsed addresses_no_post_dir \ --source_pk=id output_report_file=./output/source_report.xlsx \ --reference_pk=id \ --source_address_number=address_number \ --source_address_number_suffix=address_number_suffix \ --source_street_name_pre_directional=street_name_pre_directional \ --source_street_name_pre_type=street_name_pre_type \ --source_street_name=street_name \ --source_street_name_post_type=street_name_post_type \ -limit=-1

      
      

   2. Create table addresses_with_post_dir

      Code Block
      language sql firstline 1 title addresses_with_post_dir linenumbers true
      CREATE TABLE addresses_with_post_dir AS SELECT * FROM addresses_parsed WHERE NOT id IN (SELECT id FROM addresses_no_post_dir);

      
      

5. Output table
   1. This step generates a new table. The new table contains all of the fields and records of the input table excluding any that have a post direction value and corresponding addresses with streets without the post direction.
6. Artifacts (**)
   1. addresses_no_post_dir.csv - This is an archive of the records that made it past the filter to the next step.
   2. addresses_with_post_dir.csv - This is an archive of the records that did not make it past the filter. This artifact is part of the exclusion set of addresses that are set aside for later review.

•  

  Step 2.5 - Exclude addresses not in reference (Optional)

 This optional step excludes from the source dataset any addresses that are not also found in the parsed reference dataset.

 For an understanding on how this step handles duplicates in either the source or reference, see the comments in EAS Issue 281, Refine the filtering regime for address data to be bulk-loaded .

1. Script Name (*) is_address_in_reference.py
2. URL https://bitbucket.org/sfgovdt/sfgis-general-address-parser/src/master/is_address_in_reference.py
3. Important arguments
   1. source_output_field - The name of a new integer field that will store the results of the step.
4. Example usage

   1. Add field to flag if address is found in reference  (^++<odbc>)

      Code Block
      language text firstline 1 title is_address_in_reference linenumbers true
      python is_address_in_reference.py \ --odbc_server=<odbc-server> \ --odbc_port=<odbc-port> \ --odbc_database=<odbc-database> \ --odbc_uid=<odbc-uid> \ --odbc_pwd=<odbc-pwd> \ --source_table=addresses_no_post_dir \ --reference_table=reference_parsed \ --source_pk=id \ --reference_pk=id \ --source_address_number=address_number \ --source_address_number_suffix=address_number_suffix \ --source_street_name_pre_directional=street_name_pre_directional \ --source_street_name_pre_type=street_name_pre_type \ --source_street_name=street_name \ --source_street_name_post_type=street_name_post_type \ --source_street_name_post_directional=street_name_post_directional \ --source_subaddress_type=subaddress_type \ --source_subaddress_identifier=subaddress_identifier \ --source_place_name=place_name \ --reference_address_number=address_number \ --reference_address_number_suffix=address_number_suffix \ --reference_street_name_pre_directional=street_name_pre_directional \ --reference_street_name_pre_type=street_name_pre_type \ --reference_street_name=street_name \ --reference_street_name_post_type=street_name_post_type \ --reference_street_name_post_directional=street_name_post_directional \ --reference_subaddress_type=subaddress_type \ --reference_subaddress_identifier=subaddress_identifier \ --reference_place_name=place_name \ --source_output_field=in_reference \ --limit=-1

      
      

   2. Create and archive table addresses_in_reference

      Code Block
      language sql firstline 1 title addresses_in_reference linenumbers true
      CREATE TABLE addresses_in_reference AS SELECT * FROM addresses_no_post_dir WHERE in_reference = 1;

      
      

   3. Create and archive table addresses_not_in_reference

      Code Block
      language sql firstline 1 title addresses_not_in_reference linenumbers true
      CREATE TABLE addresses_not_in_reference AS SELECT * FROM addresses_no_post_dir WHERE in_reference = 0;

      
      

5. Output table
   1. This step generates a new table. The name of the new table is passed as a required command line argument.
6. Output fields
   1. A new field that tells if the source address was found in the reference set (1), not found (0), or not yet checked (-1). The name of the new field is passed as a required command line argument.
7. Artifacts (**)
   1. addresses_in_reference.csv - This is an archive of the records that made it past the filter to the next step.
   2. addresses_not_in_reference.csv - This is an archive of the records that did not make it past the filter. This artifact is part of the exclusion set of addresses that are set aside for later review.

...

Anchor
stage3 stage3

Stage 3 - Geocode and filter

•  

  Step 3.1 - Geocode source dataset

1. Input - addresses_to_geocode.csv
2. Output - addresses_geocoded.shp

3. Detailed Substeps

   1. Create folder for storing all input and output artifacts for this iteration of the Bulk Loader process.

      • For example, R:\Tec\..\Eas\_Task\2018_2019\path\to\archive\bulkloader_process_YYYYMMDD

   2. Create new ArcMap map document (ArcMap 10.6.1) 

      • For example, bulkloader_YYYYMMDD.mxd

   3. Add streets (optional)

      • See StClines_20190129.shp in R:\Tec\...\Eas\_Task\2018_2019\20181128_248_DocumentBulkLoader\Data

   4. Create Personal Geodatabase

      1. Right click Home in Folder Connections in Catalog

      2. Select New → Personal Geodatabase

   5. Import CSV into File Geodatabase

      1. Right-click new personal geodatabase: Import → Table (single)

      2. Browse to addresses_to_geocode.csv

      3. Specify output table addresses_to_geocode

      4. Click OK and time on stopwatch. Wait up to 5 minutes for TOC to update.

   6. Geocode with Virtual Address Geocoder
      
      1. Right-click the table addresses_to_geocode in the ArcMap table of contents and select Geocode Addresses
      2. In the dialog 'Choose an address geocoder to use' select 'Add'. 
         1. Browse to and select R:\311\...\StClines_20150729_VirtualAddressLocator (TODO - Replace with new path)
         2. Click 'OK'
      3. Under 'Address Input Fields' select 'Multiple Fields'
         1. Street or intersection: address
         2. ZIP Code: zip
      4. Under Output
         1. Click folder icon. In popup change 'Save as type' to 'Shapefile'.

         2. Save shapefile in path dedicated to artifacts for this Bulk Loader Progress

            1. For example, R:\Tec\..\Eas\_Task\2018_2019\path\to\archive\bulkloader_YYYYMMDD\geocoder\

            2. addresses_geocoded.shp

      5. Time on stopwatch and take note of execution time.

4. Artifacts (**)

   1. bulkloader_process_YYYYMMDD.mxd

   2. addresses_geocoded.shp 

•  

  Step 3.2 - Filter geocoded dataset

The purpose of this step is to filter geocoded addresses based on their geocoding score and status.

• The geocoding score has a maximum of 100 points. This step keeps addresses with a score of 100.
• The geocoding process also applies of a status value of either 'M' for matched, 'T' for tied with another address, and 'U' for unmatched.
• This step filters out any addresses that are tied with another address (status of 'T'), even if the score is 100. 

1. Input - addresses_geocoded.shp

2. Detailed Substeps

   1. Open the ArcMap document created in the previous step.

   2. Filter matched addresses (status = 'M') with score of 100.

   3. In the Table of Contents right-click the shapefile from previous step, e.g. addresses_geocoded.shp, and select Open Attributes Table.

   4. Click the first icon in the menu bar (top-left) and select Select By Attributes.

   Enter the following WHERE clause to select matched address with a score of 100:

   Geocode with Virtual Address Geocoder
   
   1. Right-click the table addresses_to_geocode in the ArcMap table of contents and select Geocode Addresses
   2. In the dialog 'Choose an address geocoder to use' select 'Add'. 
      1. Browse to and select R:\311\...\StClines_20150729_VirtualAddressLocator (TODO - Replace with new path)
      2. Click 'OK'
   3. Under 'Address Input Fields' select 'Multiple Fields'
      1. Street or intersection: address
      2. ZIP Code: zip
   4. Under Output
      1. Click folder icon. In popup change 'Save as type' to 'Shapefile'.

      2. Save shapefile in path dedicated to artifacts for this Bulk Loader Progress

         1. For example, R:\Tec\..\Eas\_Task\2018_2019\path\to\archive\bulkloader_YYYYMMDD\geocoder\

         2. addresses_geocoded.shp

   5. Time on stopwatch and take note of execution time.

3. Artifacts (**)

   1. bulkloader_process_YYYYMMDD.mxd

   2. addresses_geocoded.shp 

•  

  Step 3.2 - Filter geocoded dataset

The purpose of this step is to filter geocoded addresses based on their geocoding score and status.

• The geocoding score has a maximum of 100 points. This step keeps addresses with a score of 100.
• The geocoding process also applies of a status value of either 'M' for matched, 'T' for tied with another address, and 'U' for unmatched.
• This step filters out any addresses that are tied with another address (status of 'T'), even if the score is 100. 

1. Input - addresses_geocoded.shp

2. Detailed Substeps

   1. Open the ArcMap document created in the previous step.

   2. Filter matched addresses (status = 'M') with score of 100.

      1. In the Table of Contents right-click the shapefile from previous step, e.g. addresses_geocoded.shp, and select Open Attributes Table.

      2. Click the first icon in the menu bar (top-left) and select Select By Attributes.

      3. Enter the following WHERE clause to select matched address with a score of 100:

         • ("Status" = 'M') AND ("Score" = 100)

1. 1. 1. Click Apply, wait for operation to complete and then close the Attributes window

   2. Save results to geocoder_matched_and_score100.shp

      1. In the Table of Contents right-click the shapefile from the previous step and select Data → Export Data

      2. Click the browse icon.

      3. In the file browser select the Save as type dropdown and select Shapefile.

      4. Save the shapefile to the artifacts folder dedicated to this iteration of the Bulk Loader Process.

   3. Filter the opposite set: status not matched ('M') or score not 100.

      1. Right-click addresses_geocoded.shp and open the attributes table.

      2. Enter the following WHERE clause:

         • NOT ("Status" = 'M')

AND

•  

  Step 4.1 - Export shapefile for Bulk Loading (entire set or subset batch)

1. Substeps
   1. Create Batch from Subset (optional)

 This example shows filtering for the second batch of 50,000 records using the counter_ field.

1. 1. 1. Right-click the layer geocoder_score_100 in the ArcMap Table of Contents and select Open Attributes Table.
      2. Click the first icon in the menu bar (top-left) and select Select By Attributes.
      3. Enter the following WHERE clause to select the current batch of 50,000 records:
         "counter_" >= 50000 AND "counter_" < 100000
      4. Click Apply, wait for operation to complete and then close the Attributes window
         •  The batch may contain less than 50,000 records due to filtering by geocoding results in the previous step.
   2. Export for Bulk Loader

      1. In the Table of Contents right-click the layer geocoder_score_100 and select Data → Export Data.

      2. Click the browse icon.

      3. In the file browser select the Save as type dropdown and select Shapefile.

      4. Save the shapefile to the artifacts folder dedicated to this iteration of the Bulk Loader Process.

 e.g. R:\Tec\..\Eas\_Task\2018_2019\path\to\archive\bulkloader_process_YYYYMMDD\bulkloader\batch_002\bulkload.shp

1. Artifacts
   1. bulkload.shp - Shapefile for loading into the Bulk Loader in the next stage.

...

 For a complete set of steps and background about the Bulk Loader, see also Running the Bulk Loader, a page dedicated to its input, operation and results.

•  

  Step 5.1 - Production-specific preparation

1. Backup Database
   1. Make a backup of the EAS database
      • See also Backup the EAS Databases

Halt Services

Warning
title Reason for halting services
These steps are being performed to facilitate immediate roll-back of the EAS database if the Bulk Load Process ends in failure.

Place EAS web application into maintenance mode

Place the Web servers into maintenance mode (SF PROD WEB, DR

2. 1. 1. • OR NOT ("Score" = 100)

1. 1. 1. Click Apply, wait for operation to complete and then close the Attributes window

   2. Save results to geocoder_matched_and_score100.shp

      1. In the Table of Contents right-click the shapefile from the previous step and select Data → Export Data

      2. Click the browse icon.

      3. In the file browser select the Save as type dropdown and select Shapefile.

      4. Save the shapefile to the artifacts folder dedicated to this iteration of the Bulk Loader Process.

   3. Filter the opposite set: status not matched ('M') or score not 100.

      1. Right-click addresses_geocoded.shp and open the attributes table.

      2. Enter the following WHERE clause:

         • NOT ("Status" = 'M') OR NOT ("Score" = 100)

   4. Save results to geocoder_notmatched_or_under100.shp. (See substeps above.)

2. Output / Artifacts

   1. geocoder_score_100.shp - Shapefile of matched addresses (status = 'M') with geocoding score of 100.

   2. geocoder_notmatched_or_under100.shp - Shapefile of non-matched addresses (unmatched or tied) or with geocoding score less than 100.

 The total number of records of the two output shapefiles should be the same as the number of records in the input shapefile.

...

1. 1. Save results to geocoder_notmatched_or_under100.shp. (See substeps above.)

2. Output / Artifacts

   1. geocoder_score_100.shp - Shapefile of matched addresses (status = 'M') with geocoding score of 100.

   2. geocoder_notmatched_or_under100.shp - Shapefile of non-matched addresses (unmatched or tied) or with geocoding score less than 100.

 The total number of records of the two output shapefiles should be the same as the number of records in the input shapefile.

Anchor
stage4 stage4

Stage 4 - Export shapefile - full set (single batch) or subset (multiple batches)

•  

  Step 4.1 - Export shapefile for Bulk Loading (entire set or subset batch)

1. Substeps
   1. Create Batch from Subset (optional)

 This example shows filtering for the second batch of 50,000 records using the counter_ field.

1. 1. 1. Right-click the layer geocoder_score_100 in the ArcMap Table of Contents and select Open Attributes Table.
      2. Click the first icon in the menu bar (top-left) and select Select By Attributes.

      3. Enter the following WHERE clause to select the current batch of 50,000 records:

         Code Block
         language text linenumbers true
         "counter_" > 50000 AND "counter_" <= 100000

         
         

      4. Click Apply, wait for operation to complete and then close the Attributes window
         •  The batch may contain less than 50,000 records due to filtering by geocoding results in the previous step.
   2. Export for Bulk Loader

      1. In the Table of Contents right-click the layer geocoder_score_100 and select Data → Export Data.

      2. Click the browse icon.

      3. In the file browser select the Save as type dropdown and select Shapefile.

      4. Save the shapefile to the artifacts folder dedicated to this iteration of the Bulk Loader Process.

 e.g. R:\Tec\..\Eas\_Task\2018_2019\path\to\archive\bulkloader_process_YYYYMMDD\bulkloader\batch_NNN\bulkload.shp

1. Artifacts
   1. bulkload.shp - Shapefile for loading into the Bulk Loader in the next stage.

Anchor
stage5 stage5

Stage 5 - Run the Bulk Loader

 For a complete set of steps and background about the Bulk Loader, see also Running the Bulk Loader, a page dedicated to its input, operation and results.

•  

  Step 5.1 - Disable front-end access to EAS

1. Notify relevant recipients that the Bulk Loader Process is starting

2. Disable web service on <environment>_WEB (SF DEV WEB, SF QA WEB, SF PROD WEB)

   Code Block
   language bash linenumbers true
   cd /var/www/html sudo ./set_eas_mode.sh MAINT

   
   

   1. Browse to web site, http://eas.sfgov.org/, to confirm the service has stopped. (Expect to see message that EAS is currently out of service.)

•  

  Step 5.2 - Non-production-specific preparation

1. Restore Database

   1. Restore database from latest daily production backup

•  

  Step 5.3 - Production-specific preparation

1. Halt Services

   Warning
   title Reason for halting services
   These steps are being performed to facilitate immediate roll-back of the EAS database if the Bulk Load Process ends in failure

   
   

   1. SKIP Turn off the replication server

      1. Disable database replication by shutting down the database service on the replication server (DR PROD DB).

         
         

         Code Block
         language bash title Stop PostgreSQL linenumbers true
         #sudo -u postgres -i #/usr/pgsql-9.0/bin/pg_ctl -D /data/9.0/data stop

         
         

   2. Turn off downstream database propagation service(s)

      1. Suspend downstream replication to internal business system database (SF PROD WEB).

         Code Block
         language bash title

      Place EAS web application into maintenance modelinenumberstrue

      cd /var/www/html
      sudo ./set_eas_mode.sh MAINT

      Turn off the replication server

      Disable database replication by shutting down the database service on the replication server (DR PROD DB)

      1. stop xmit linenumbers true

         sudo /var/www/html/eas/bin/xmit_change_notifications.bsh stop

         
         

2. Backup Database

   • See also Backup the EAS Databases

sudo -u postgres -i
/home/dba/scripts/dbbackup.sh > /var/tmp/dbbackup.log # this step takes about 2 minutes
ls -l /var/tmp # ensure the log file is 0 bytes
ls -la /mnt/backup/pg/daily/easproddb.sfgov.org-* # the timestamp on the last file listed should match timestamp of backup
exit # logout of user postgres when done

•  

  Step 5.3 - Database Preparation 

1. Connect to the database, <environment>_DB, and clear any leftover records from previous Bulk Loader batches.

   Code Block
   language

   bash

   sql title

   Stop PostgreSQL

   TRUNCATE linenumbers true

   sudo -u postgres -i
   /usr/pgsql-9.0/bin/pg_ctl -D /data/9.0/data stop

   Turn off downstream database propagation service(s)

   Suspend downstream replication to internal business system database (SF PROD WEB).

   TRUNCATE bulkloader.address_extract, bulkloader.blocks_nearest;

   
   

   Code Block
   language

   text

   sql title

   stop xmitConnect to the database, <environment>_DB, and clear any leftover records from previous Bulk Loader batches

   VACUUM linenumbers true

   sudo /var/www/html/eas/bin/xmit_change_notifications.bsh stop

•  

  Step 5.2 - Non-production-specific preparation

1. Restore Database
   1. Restore database from latest daily production backup

•  

  Step 5.3 - Database Preparation

1. VACUUM FULL ANALYZE bulkloader.address_extract;

   
   

   Code Block
   language sql title VACUUM linenumbers true
   VACUUM FULL ANALYZE bulkloader.blocks_nearest;

   
   

2. Make note of EAS record counts before the Bulk Loading operation.

   Code Block
   language sql firstline 1 title truncateRecord Counts linenumbers true
   TRUNCATE bulkloader.address_extract, bulkloader.blocks_nearest;

   Code Block
   language sql title vacuum linenumbers true
   VACUUM FULL ANALYZE bulkloader.address_extract;

   Code Block
   language sql title vacuum linenumbers true
   VACUUM FULL ANALYZE bulkloader.blocks_nearest;

   Make note of EAS record counts before the Bulk Loading operation.

   Code Block
   language sql firstline 1 title Record Counts linenumbers true
   SELECT schemaname,relname,n_live_tup FROM pg_stat_user_tables ORDER BY schemaname,relname,n_live_tup

   Make note of the database partition size on the file system.

   Code Block
   language bash firstline 1 title disk usage linenumbers true
   df /data

•  

  Step 5.4 - Transfer Shapefiles 

1. Transfer the bulkload.shp shapefile from Stage 4 to an EAS automation machine, <environment>_AUTO.
2. Substitute <environment> with one of the relevant environments: SF_DEV, SF_QA, SF_PROD, SD_PROD.
3. Copy the shapefile to the folder C:\apps\eas_automation\app_data\data\bulkload_shapefile.

•  

  Step 5.5 - Run Bulk Loader

1. Open a command prompt and change folders:

   • cd C:\apps\eas_automation\automation\src

2. Run the step to stage the address records:

   • python job.py --job stage_bulkload_shapefile --env SF_DEV --action EXECUTE --v

3. Run the step to bulk load the address records:

   python job.py --job bulkload --env SF_DEV --action EXECUTE --v

   SELECT schemaname,relname,n_live_tup FROM pg_stat_user_tables ORDER BY schemaname,relname,n_live_tup

   1. Save results in artifact as record_counts_before.csv 
   2. Also save results in Excel spreadsheet artifact as BulkLoader_Process_YYYYMMDD.xlsx

4. Make note of the database partition size on the file system at the current point in time.

   Code Block
   language bash firstline 1 title disk usage linenumbers true
   date; df /data # 1st of 3

   
   

•  

  Step 5.4 - Transfer Shapefiles 

1. Transfer the bulkload.shp shapefile from Stage 4 to an EAS automation machine, <environment>_AUTO.
2. Substitute <environment> with one of the relevant environments: SF_DEV, SF_QA, SF_PROD, SD_PROD.
3. Copy the shapefile to the folder C:\apps\eas_automation\app_data\data\bulkload_shapefile.

•  

  Step 5.5 - Run Bulk Loader

1. Open a command prompt and change folders:

   Code Block
   language bash linenumbers true
   cd C:\apps\eas_automation\automation\src

   
   

2. Run the step to stage the address records:

   Code Block
   language bash linenumbers true
   python job.py --job stage_bulkload_shapefile --env <environment> --action EXECUTE --v python job.py --job stage_bulkload_shapefile --env SF_DEV --action EXECUTE --v python job.py --job stage_bulkload_shapefile --env SF_QA --action EXECUTE --v python job.py --job stage_bulkload_shapefile --env SF_PROD --action EXECUTE --v

   
   

3. Run the step to bulk load the address records

   
   

   Code Block
   language bash linenumbers true
   python job.py --job bulkload --env <environment> --action EXECUTE --v python job.py --job bulkload --env SF_DEV --action EXECUTE --v python job.py --job bulkload --env SF_QA --action EXECUTE --v python job.py --job bulkload --env SF_PROD --action EXECUTE --v

   
   

4.  To calculate the time it took to run the Bulk Loader look at the timestamps in the output or use a stopwatch or clock to time the operation.

   
   

5. Save Bulk Loader command line output artifact as bulk_loader_CLI_output.txt

•  

  Step 5.6 - Analysis Anchoranalysisanalysis - Analysis

  Anchor
  analysis analysis

1. Make note of the database partition size on the file system at this point. Compare with size of partition prior to loading to get the total disk space used as a result of running the Bulk Loader.

   Code Block
   language bash firstline 1 title disk usage linenumbers true
   date; df /data # 2nd of 3

   
   

2. Make note of EAS record counts after the Bulk Load operation.

   Code Block
   language sql firstline 1 title Record Counts linenumbers true
   SELECT schemaname,relname,n_live_tup FROM pg_stat_user_tables ORDER BY schemaname,relname,n_live_tup

   A comparison of

   
   

   • Save results as artifact record_counts_after.csv
   • Also save results in Excel spreadsheet artifact as BulkLoader_Process_YYYYMMDD.xlsx

   • In the spreadsheet, calculate the difference between the 'before' and 'after' record counts. The results will indicate the number of new base addresses added to the table `public.address_base` and the number of new addresses and units added to the table `public.addresses`.

   •  See dedicated Bulk Loader page, Running the Bulk Loader, for more analysis options

   .
   

   Make note of the database partition size on the file system. Compre with size of partition prior to loading to get the total disk space used as a result of running the Bulk Loader.

   Code Block
   language bash firstline 1 title disk usage linenumbers true
   df /data

   Query and make note of totals in the bulkloader.address_extract table. The results here will be used to cross check the results in the next stage.Count/view new base addresses added to the EAS

   • .

Anchor
stage6 stage6

Stage 6 - Extract results

•  

  Step 6.1 - Archive exceptions

1. Archive the entire address_extract table.

   1. Use a query tool such as pgAdmin to query and save the table as a CSV file.

      Code Block
      language sql firstline 1 title Count/view new base addressesaddress_extract linenumbers true
      SELECT COUNT(*) FROM bulkloader.address_extract WHERE NOT (street_segment_id IS NULL) SELECT * FROM bulkloader.address_extract WHERE NOT (street_segment_id IS NULL)

      Count/view unit addresses (some were already there, some are new)

      ;

      
      

   2. Save the file in the network folder dedicated to artifacts for the Bulk Loader iteration.
      1. Save as artifact address_extract.csv
2. Archive the addresses that raised exceptions during the Bulk Loader process

   1. Query subtotals

      Code Block
      language sql firstline 1 title Count/view unit addressesexception_text_counts linenumbers true
      SELECT COUNTexception_text, Count(*) FROM bulkloader.address_extract WHEREGROUP NOTBY (address_id IS NULL) exception_text ORDER BY exception_text;

      1. Save artifact as exception_text_counts.csv

   2. Query all exception text records

      Code Block
      language sql firstline 1 title exception_text linenumbers true
      SELECT * FROM bulkloader.address_extract WHERE NOT (addressexception_idtext IS NULL)

...

•  

  Step 6.1 - Archive exceptions

1. Archive the entire address_extract table.
   1. Use a query tool such as pgAdmin to query and save the table as a CSV file.

      ORDER BY exception_text, id;

      1. Save artifact as exception_text.csv
2. Artifacts
   1. address_extract.csv - Results of every address submitted to the Bulk Loader.
   2. exception_text_counts.csv - Counts of the records that were not loaded due to the error indicated in the 'exception_text' field.
   3. exception_text.csv - Subset of the just the records that were not loaded due to the error indicated in the 'exception_text' field

•  

  Step 6.2 - Archive unique EAS change_request_id associated with the Bulk Load

1. Get the unique EAS change_request_id created by the Bulk Load operation. The value of <change_request_id> will be used in the next steps to count addresses added to the EAS.
   

   1. Query the 'public.change_requests' table for the new 'change_request_id' value.

   2. Query the 'bulkloader.address_extract' table for any value in the 'exception_text' field

      Code Block
      language sql firstline 1 title bulkloader.address_extract linenumbers true
      SELECT * FROM 'bulkloader.address_extract';

   3. Save the file in the network folder dedicated to artifacts for the Bulk Loader iteration.
      • For example, R:\Tec\..\Eas\_Task\path\to\archive\bulkloader_YYYYMMDD\bulkloader\batch_002\address_extract.csv
   Archive the addresses that raised exceptions during the Bulk Loader run

   1. change_request_id linenumbers true

      SELECT change_request_id FROM public.change_requests WHERE requestor_comment LIKE 'bulk load change request' ORDER BY change_request_id DESC LIMIT 1;

      
      

   2. Save artifact as change_request_id.csv
2. Artifacts
   1. change_request_id.csv - The unique EAS change_request_id created by the Bulk Load operation.

•  

  Anchor
  step6.3 step6.3

  Step 6.3 - Archive new EAS addresses records

1. Get all the address records (including units) added to the EAS during the Bulk Loader operation..
   Get the unique EAS change_request_id created by the Bulk Load operation. The value of <change_request_id> will be used in the next steps to count addresses added to the EAS.
   

   1. Query the public.addresses table on the new change_request_id value.

      Code Block
      language sql firstline 1 title exception_textaddresses linenumbers true
      SELECT * FROM bulkloader.address_extract public.addresses WHERE NOT(exception_text IS NULL) ORDER BY exception_text;

   2. Save the file in the network folder dedicated to artifacts for the Bulk Loader iteration.
      • For example, R:\Tec\..\Eas\_Task\path\to\archive\bulkloader_YYYYMMDD\bulkloader\batch_002\exceptions.csv
2. Artifacts
   1. address_extract.csv - Results of every address submitted to the Bulk Loader.
   2. exception_text.csv - Subset of the just the records that were not loaded due to the error indicated in the 'exception_text' field.

•  

  Step 6.2 - Archive unique EAS change_request_id associated with the Bulk Load

1. 1. Query the 'public.change_requests' table for the new 'change_request_id'

      activate_change_request_id = <change_request_id>;

      
      

   2. Save artifact as addresses.csv
2. Extract sample unit address from the output
   1. Pick a random record from the results where unit_num is not NULL. Gather the value in the address_base_id field.
   2. Construct a URL from this value like this: http://eas.sfgov.org/?address=NNNNNN
      • Where NNNNNN is the value from the address_base_id field.
   3. Make note of this URL for use in Step 7 when testing EAS after services are restored.
3.  Artifacts
   1. addresses.csv - All the address records (including units) added to the EAS during the Bulk Loader operation.

•  

  Anchor
  step6.4 step6.4

  Step 6.4 - Archive new EAS address_base records

1. Get all the base records added to the EAS during the Bulk Loader operation.
   

...

1. Get all

   1. Query the public.address_base table on the new change_request_id value.

      Code Block
      language sql firstline 1 title changeaddress_request_idbase linenumbers true
      SELECT activate_change_request_id, address_id, public.address_base.* FROM public.changeaddress_requestsbase, public.addresses WHERE requestor_comment LIKE 'bulk load change request' ORDER BY public.address_base.address_base_id = public.addresses.address_base_id AND public.addresses.address_base_flg = TRUE AND public.addresses.activate_change_request_id DESC LIMIT 1= <change_request_id>;

      
      

   2. Save the file in the network folder dedicated to artifacts for the Bulk Loader iteration.
      • For example, R:\Tec\..\Eas\_Task\path\to\archive\bulkloader_YYYYMMDD\bulkloader\batch_002\change_request_id.csv
2. Artifacts
   1. change_request_id.csv - The unique EAS change_request_id created by the Bulk Load operation.

...

1. addresses.csv - All the address records (including units) added to the EAS during the Bulk Loader operation.

•  

  Step 6.5 - Cross check results

Compare the results of Stage 5 with the results from Stage 6.

1. The number of base addresses found in the Stage 5 Analysis should be identical to the number of base addresses found in Step 6.3.

2. The number of addresses found in the Stage 5 Analysis should be less than or equal to the number of addresses listed in Step 6.4. (The Bulk Loader does not provide enough information in the the bulkloader.address_extract table to determine the exact number of new addresses added. But there is enough information to determine an upper limit.)

...

Step 7.1 - Database Cleanup

...

Connect to the database, <environment>_DB, and clear temporary records from the latest Bulk Loader batch.

TRUNCATE bulkloader.address_extract, bulkloader.blocks_nearest;

VACUUM FULL ANALYZE bulkloader.address_extract;

TRUNCATE bulkloader.address_extract, bulkloader.blocks_nearest;

...

Step 7.2 - On Failure Restore Database

1. If the Bulk Loader failed and corrupted any data then restore from the database backup.
   1. Follow these steps to restore from backup.

•  

  Step 7.3 - Restore Services (Production Only)

1. 1. artifact as address_base.csv
2. Extract sample base address from the output
   1. Pick a random record from the results. Gather the value in the address_base_id field.
   2. Construct a URL from this value like this: http://eas.sfgov.org/?address=NNNNNN
      • Where NNNNNN is the value from the address_base_id field.
   3. Make note of this URL for use in Step 7 when testing EAS after services are restored.
3. Artifacts
   1. address_base.csv - All the base records added to the EAS during the Bulk Loader operation.
   
   

   Query the public.address_base table on the new change_request_id value.

   Code Block
   language sql firstline 1 title public.address_base linenumbers true
   SELECT activate_change_request_id, address_id, public.address_base.* FROM public.address_base, public.addresses WHERE public.address_base.address_base_id = public.addresses.address_base_id AND public.addresses.address_base_flg = TRUE AND public.addresses.activate_change_request_id = <change_request_id>;

4. Save the file in the network folder dedicated to artifacts for the Bulk Loader iteration.
   • For example, R:\Tec\..\Eas\_Task\path\to\archive\bulkloader_YYYYMMDD\bulkloader\batch_002\address_base.csv
5. Artifacts
   1. address_base.csv - All the base records added to the EAS during the Bulk Loader operation.

...

Query the public.addresses table on the new change_request_id value.

SELECT * FROM public.addresses
WHERE activate_change_request_id = <change_request_id>;

...

• For example, R:\Tec\..\Eas\_Task\path\to\archive\bulkloader_YYYYMMDD\bulkloader\batch_002\addresses.csv

1. If the Bulk Loader Process was run on the production server then restore services

   1. Turn on production-to-replication service

      Re-enable database replication by restarting the database service on the replication server (DR PROD DB).

      Code Block
      language bash title Stop PostgreSQL linenumbers true
      sudo -u postgres -i /usr/pgsql-9.0/bin/pg_ctl -D /data/9.0/data start

      Turn on downstream database propagation service(s)

      Resume downstream replication to internal business system database (SF PROD WEB).

      Code Block
      language text firstline 1 title start xmit
      sudo /var/www/html/eas/bin/xmit_change_notifications.bsh start

      Enable front-end access to EAS

      Place the Web servers into live mode (SF PROD WEB, DR PROD WEB).

      Code Block

      language	bash
      linenumbers	true

      cd /var/www/html sudo ./set_eas_mode.sh LIVE

•  

  Step 6.5 - Cross check results

Compare the results of Stage 5 with the results from Stage 6.

1. The number of addresses found in the Step 5.6 (2) should be identical to the number of addresses found in Step 6.3.

2. The number of base addresses found in the Step 5.6 (2) should be identical to the number of base addresses found in Step 6.4.

Anchor
stage7 stage7

Stage 7 - Cleanup and Restoration

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  Step 7.1 - Database Cleanup

1. Connect to the database, <environment>_DB, and clear address_extract records from the latest Bulk Loader batch. Make note of final disk usage tally.

TRUNCATE bulkloader.address_extract, bulkloader.blocks_nearest;

VACUUM FULL ANALYZE bulkloader.address_extract;

VACUUM FULL ANALYZE bulkloader.blocks_nearest;

date; df /data # 3rd of 3
# Optional step: archive output to 'df.txt' artifact
exit

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  Step 7.2 - Clean automation machine

1. Return to automation machine and remove shapefile from 'bulkload_shapefile' folder.
2. Logout of automation machine. 

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  Step 7.3 - On Failure Restore Database

1. If the Bulk Loader failed and corrupted any data then restore from the database backup.
   1. Follow these steps to restore from backup.

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  Step 7.4 - Restore Services (Production Only)

1. SKIP Turn on production-to-replication service

   • Re-enable database replication by restarting the database service on the replication server (DR PROD DB).

     
     

     Code Block
     language bash title Stop PostgreSQL linenumbers true
     #sudo -u postgres -i #/usr/pgsql-9.0/bin/pg_ctl -D /data/9.0/data start

     
     

2. SKIP Turn on downstream database propagation service(s)

   • Resume downstream replication to internal business system database (SF PROD WEB).

     Code Block
     language bash firstline 1 title start xmit
     #sudo /var/www/html/eas/bin/xmit_change_notifications.bsh start

     
     

•  

  Anchor
  step7.5 step7.5

  Step 7.5 - Enable front-end access to EAS

1. Enable web service on <environment>_WEB (SF DEV WEB, SF QA WEB, SF PROD WEB)

   Code Block
   language bash linenumbers true
   cd /var/www/html sudo ./set_eas_mode.sh LIVE exit

   
   

2. Browse to website, http://eas.sfgov.org/, and review sample addresses gathered in Step 6.3 and Step 6.4

3. Notify relevant recipients that the Bulk Loader Process is complete

•  

  Step 7.6 - Archive artifacts

1. List of artifacts
   1. address_base.csv
   2. address_extract.csv
   3. addresses.csv
   4. bulk_loader_CLI_output.txt
   5. change_request_id.csv
   6. df.txt
   7. exception_text.csv
   8. exception_text_counts.csv

2. Contents of progress and summary artifact, BulkLoader_Process_YYYYMMDD.xlsx

   
   

   1. Progress - This sheet contains a table of relevant totals for each batch

      1. Batch number

      2. Batch date

      3. Input record counts

      4. New base record counts

      5. New unit record counts

      6. Sample addresses

         
         

   2. Email Jobs - This sheet contains a table of details related to the weekly 'Address Notification Report' automated email job

      1. Batch range

      2. Record count in batch range

      3. Email Timestamp

      4. Total record counts in email

      5. Subtotal of records generated as a result of the Bulk Loader

      6. Size of email attachment

         
         

   3. Batch N - This sheet tracks the before and after record counts for all tables in the EAS database. There is a sheet for each batch loaded. Within each sheet is a section for the 'before' records, a section for the 'after' record counts, and a 'diff' column showing the change in record counts.

END OF STEPS

Notes

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