A new version of the CIDACS-RL linkage tool suitable to a cluster computing environment.

Related tags

Deep Learningfully-distributed-cidacs-rl
Overview

Fully Distributed CIDACS-RL

The CIDACS-RL is a brazillian record linkage tool suitable to integrate large amount of data with high accuracy. However, its current implementation relies on a ElasticSearch Cluster to distribute the queries and a single node to perform them through Python Multiprocessing lib. This implementation of CIDACS-RL tool can be deployed in a Spark Cluster using all resources available by Jupyter Kernel still using the ElasticSearch cluster, becaming a fully distributed and cluster based solution. It can outperform the legacy version of CIDACS-RL either on multi-node or single node Spark Environment.

config.json

Almost all the aspects of the linkage can be manipulated by the config.json file.

Section Sub-section Field (datatype) Field description
General info index_data (str<'yes', 'no'>) This flag says if the linkage process includes the indexing of a data set into elastic search. Constraints: string, it can assume the values "yes" or "no".
General info es_index_name (str<ES_VALID_INDEX>) The name of an existing elasticsearch index (if index_data is 'no') or a new one (if index_data is 'yes'). Constraints: string, elasticsearch valid.
General info es_connect_string (str<ES_URL:ES_PORT>) Elasticsearch API address. Constraints: string, URL format.
General info query_size (int) Number of candidates output for each Elasticsearch query. Constraints: int.
General info cutoff_exact_match (str<0:1 number>) Cutoff point to determine wether a pair is an exact match or not. Constraints: str, number between 0 and 1.
General info null_value (str) Value to replace missings on both data sets involved. Constraints: string.
General info temp_dir (str) Directory used to write checkpoints for exact match and non-exact match phases. Constraints: string, fully qualified path.
General info debug (str<'true', 'false'>) If it is set as "true", all records found on exact match will be queried again on non-exact match phase.
Datasets info Indexed dataset path (str) Path for csv or parquet folder of dataset to index.
Datasets info Indexed dataset extension (str<'csv', 'parquet'>) String to determine the type of data reading on Spark.
Datasets info Indexed dataset columns (list) Python list with column names involved on linkage.
Datasets info Indexed dataset id_column_name (str) Name of id column.
Datasets info Indexed dataset storage_level (str<'MEMORY_AND_DISK', 'MEMORY_ONLY'>) Directive for memory allocation on Spark.
Datasets info Indexed dataset default_paralelism (str<4*N_OF_AVAILABLE_CORES>) Number of partitions of a given Spark dataframe.
Datasets info tolink dataset path (str) Path for csv or parquet folder of dataset to index.
Datasets info tolink dataset extension (str<'csv', 'parquet'>) String to determine the type of data reading on Spark.
Datasets info tolink dataset columns (list) Python list with column names involved on linkage.
Datasets info tolink dataset id_column_name (str) Name of id column.
Datasets info tolink dataset storage_level (str<'MEMORY_AND_DISK', 'MEMORY_ONLY'>) Directive for memory allocation on Spark.
Datasets info tolink dataset default_paralelism (str<4*N_OF_AVAILABLE_CORES>) Number of partitions of a given Spark dataframe.
Datasets info result dataset path (str) Path for csv or parquet folder of dataset to index.
Comparisons label1 indexed_col (str) Name of first column to be compared on indexed dataset
Comparisons label1 tolink_col (str) Name of first column to be compared on tolink dataset
Comparisons label1 must_match (str<'true', 'false'>) Set if this pair of columns are included on exact match phase
Comparisons label1 should_match (str<'true', 'false'>) Set if this pair of columns are included on non-exact match phase
Comparisons label1 is_fuzzy (str<'true', 'false'>) Set if this pair of columns are included on fuzzy queries for non-exact match phase
Comparisons label1 boost (str) Set the boost/weight of this pair of columns on queries
Comparisons label1 query_type (str<'match', 'term'>) Set the type of matching for this pair of columns on non-exact match phase
Comparisons label1 similarity (str<'jaro_winkler', 'overlap', 'hamming'> Set the similarity to be calculated between the values of this pair of columns
Comparisons label1 weight (str) Set the weight of this pair of columns.
Comparisons label1 penalty (str) Set the penalty of the overall similarity in case of missing value(s).
Comparisons label2 ... ...

config.json example


{
 'index_data': 'no',
 'es_index_name': 'fd-cidacs-rl',
 'es_connect_string': 'http://localhost:9200',
 'query_size': 100,
 'cutoff_exact_match': '0.95',
 'null_value': '99',
 'temp_dir': '../../../0_global_data/fd-cidacs-rl/temp_dataframe/',
 'debug': 'false',
 
 'datasets_info': {
    'indexed_dataset': {
        'path': '../../../0_global_data/fd-cidacs-rl/sinthetic-dataset-A.parquet',
        'extension': 'parquet',
        'columns': ['id_cidacs_a', 'nome_a', 'nome_mae_a', 'dt_nasc_a', 'sexo_a'],
        'id_column_name': 'id_cidacs_a',
        'storage_level': 'MEMORY_ONLY',
        'default_paralelism': '16'},
    'tolink_dataset': {
        'path': '../../../0_global_data/fd-cidacs-rl/sinthetic-datasets-b/sinthetic-datasets-b-500000.parquet',
        'extension': 'parquet',
        'columns': ['id_cidacs_b', 'nome_b', 'nome_mae_b', 'dt_nasc_b', 'sexo_b'],
        'id_column_name': 'id_cidacs_b',
        'storage_level': 'MEMORY_ONLY',
        'default_paralelism': '16'},
    'result_dataset': {
        'path': '../0_global_data/result/500000/'}},
        
 'comparisons': {
    'name': {
        'indexed_col': 'nome_a',
        'tolink_col': 'nome_b',
        'must_match': 'true',
        'should_match': 'true',
        'is_fuzzy': 'true',
        'boost': '3.0',
        'query_type': 'match',
        'similarity': 'jaro_winkler',
        'weight': 5.0,
        'penalty': 0.02},
    'mothers_name': {
       'indexed_col': 'nome_mae_a',
       'tolink_col': 'nome_mae_b',
       'must_match': 'true',
       'should_match': 'true',
       'is_fuzzy': 'true',
       'boost': '2.0',
       'query_type': 'match',
       'similarity': 'jaro_winkler',
       'weight': 5.0,
       'penalty': 0.02},
  'birthdate': {
       'indexed_col': 'dt_nasc_a',
       'tolink_col': 'dt_nasc_b',
       'must_match': 'false',
       'should_match': 'true',
       'is_fuzzy': 'false',
       'boost': '',
       'query_type': 'term',
       'similarity': 'hamming',
       'weight': 1.0,
       'penalty': 0.02},
  'sex': {
       'indexed_col': 'sexo_a',
       'tolink_col': 'sexo_b',
       'must_match': 'true',
       'should_match': 'true',
       'is_fuzzy': 'false',
       'boost': '',
       'query_type': 'term',
       'similarity': 'overlap',
       'weight': 3.0,
       'penalty': 0.02}}}

Running in a Standalone Spark Cluster

Read more: https://github.com/elastic/elasticsearch-hadoop https://www.elastic.co/guide/en/elasticsearch/hadoop/current/spark.html https://search.maven.org/artifact/org.elasticsearch/elasticsearch-spark-30_2.12 If you intend to run this tool into a single node Spark environment, consider to include this in you spark-submit or spark-shell command line


pyspark --packages org.elasticsearch:elasticsearch-spark-30_2.12:7.14.0 --conf spark.es.nodes="localhost" --conf spark.es.port="9200"

If you are running into a Spark Cluster under JupyterHUB kernels, try to add this kernel or edit an existing one:


{
	 "display_name": "Spark3.3",
	  "language": "python",
	   "argv": [
		     "/opt/bigdata/anaconda3/bin/python",
		       "-m",
		         "ipykernel",
			   "-f",
			     "{connection_file}"
			      ],
			       "env": {
				         "SPARK_HOME": "/opt/bigdata/spark",
					   "PYTHONPATH": "/opt/bigdata/spark/python:/opt/bigdata/spark/python/lib/py4j-0.10.9.2-src.zip",
					     "PYTHONSTARTUP": "/opt/bigdata/spark/python/pyspark/python/pyspark/shell.py",
					       "PYSPARK_PYTHON": "/opt/bigdata/anaconda3/bin/python",
					         "PYSPARK_SUBMIT_ARGS": "--master spark://node1.sparkcluster:7077 --packages org.elasticsearch:elasticsearch-spark-30_2.12:7.14.0 --conf spark.es.nodes=['node1','node2'] --conf spark.es.port='9200' pyspark-shell"
						  }
}

Some advices for indexed data and queries

  • Every col should be casted as string (df.withColumn('column', F.col('column').cast(string')))
  • Date type columns will not be proper indexed as string, except if some preprocessing step tranform it from yyyy-MM-dd to yyyyMMdd.
  • All the nodes of elasticsearch cluster must be included on --packages configuration.
  • Term queries are good to well structured variables, such as CPF, dates, CNPJ, etc.
Owner
Robespierre Pita
AI Researcher
Robespierre Pita
GitHub Repository
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