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Set up the requirement and docker-compose. 1. Create requirement.txt file. pandas==1.2.4 elasticsearch==7.15.0 python-decouple==3.5 urllib3==1.25.11 mlxtend==0.19.0 networkx==2.6.3 matplotlib==3.5.1 prefixspan==0.5.2 2. Create Docker file. FROM continuumio/miniconda3 WORKDIR /usr/app RUN mkdir result RUN mkdir src EXPOSE 8888 RUN pip install --no-cache-dir --upgrade pip COPY ./requirements.txt ...

match It performs a full text search. Elasticsearch will look for these terms in the documents as long as all or one of these terms are found within the documents, and the match query will send these results back, it doesn't care about the order or the proximity. So it pulls up documents with search terms that are scatterd across the documents. GET news_headlines/_search { "query": { #I want to ..

Document : Data is stored as document that share similar traits are grouped into an index. User ↓ ⇢ search something Elasticsearch ↓ ⇢ retrieves relevant documents, then Elasticsearch presents them as search results Search Results Precision : The dots inside the white circle, the portion of the retrived data actually relevant to the search query. All the retrived results to be a perfect match to..

Customer → Server(1) ↓ ⇢ A customer serches for a product on my app ⇡ The request is sent to the server My shopping mall website(app) ↓↑ ⇢ It connected to a database which contains all my product data along with other data we're collecting from the app Database → Server(2) ↓ ⇡ looks up the product within the database ↓ ⇢ product info is sent back to the server Server(3) ↓ ⇢ product info can be r..

vi docker-compose.yml To get Elasticsearch cluster and kibana up and running in Docker, you can use Docker Compose. If you don’t want to expose port 9200 and instead use a reverse proxy, replace 9200:9200 with 127.0.0.1:9200:9200 in the docker-compose.yml file. Elasticsearch will then only be accessible from the host machine itself(www.elastic.co/guide/en/elasticsearch/reference/current/docker.h..