Handling Large Datasets
When dealing with large datasets, it is crucial to split your bulk requests into smaller batches to avoid overwhelming Elasticsearch. Here’s an example in Python:
from elasticsearch import Elasticsearch, helpers
import json
# Elasticsearch connection
es = Elasticsearch(["http://localhost:9200"])
# Load large dataset (assuming it's in a JSON file)
with open("large_dataset.json") as f:
data = json.load(f)
# Prepare bulk actions
actions = [
{ "_index": "myindex", "_source": doc }
for doc in data
]
# Split actions into batches and index
batch_size = 1000
for i in range(0, len(actions), batch_size):
helpers.bulk(es, actions[i:i + batch_size])
Using the Elasticsearch Bulk API for High-Performance Indexing
Elasticsearch is a powerful search and analytics engine designed to handle large volumes of data. One of the key techniques to maximize performance when ingesting data into Elasticsearch is using the Bulk API. This article will guide you through the process of using the Elasticsearch Bulk API for high-performance indexing, complete with detailed examples and outputs.
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