How to create and populate a new index on an already existing elasticsearch server.

Elasticsearch databases are great for quick searches. Let’s imagine we already have a pandas dataframe ready, data_for_es, to pop into an index and be easily search.

Connect to elasticsearch host

We can easily connect to our host using the elasticsearch library.

	# configure elasticsearch
	config = {
	    'host': 'XXX.XX.X.XXX'
	es = elasticsearch.Elasticsearch([config,], timeout=300)

Create new index

Choose the number of shards and replicas your index requires. Elasticsearch divides the data into different shards. Each shard is replicated across nodes.

Mapping tells elasticsearch what kind of data each field contains. analyzed or not_analyzed refers whether a string is analysed before it is indexed. So a field that is not_analyzed will be mapped as an exact value. Since both type of field get indexed, both are searchable.

	request_body = {
	    "settings" : {
	        "number_of_shards": 5,
	        "number_of_replicas": 1

	    'mappings': {
	        'examplecase': {
	            'properties': {
	                'address': {'index': 'not_analyzed', 'type': 'string'},
	                'date_of_birth': {'index': 'not_analyzed', 'format': 'dateOptionalTime', 'type': 'date'},
	                'some_PK': {'index': 'not_analyzed', 'type': 'string'},
	                'fave_colour': {'index': 'analyzed', 'type': 'string'},
	                'email_domain': {'index': 'not_analyzed', 'type': 'string'},
	print("creating 'example_index' index..."
	es.indices.create(index = 'example_index', body = request_body)

Prepare data

Each cell from the pandas dataframe must be input to the elasticsearch index with its metadata.

	bulk_data = []

	for index, row in data_for_es.iterrows():
	    data_dict = {}
	    for i in range(len(row)):
	        data_dict[data_for_es.columns[i]] = row[i]
	    op_dict = {
	        "index": {
	            "_index": 'example_index',
	            "_type": 'examplecase',
	            "_id": data_dict['some_PK']

Input data

Finally, the data is ready to be input to the elasticsearch index.

	res = es.bulk(index = 'example_index', body = bulk_data)

	# check data is in there, and structure in there{"query": {"match_all": {}}}, index = 'example_index')
	es_indices.get_mapping(index = 'example_index')


Always feel free to get in touch with other solutions, general thoughts or questions.