Add kibana and python examples

Author: rtancman

docker-compose.yml

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+version: '2.2'
+services:
+  es:
+    image: docker.elastic.co/elasticsearch/elasticsearch:7.9.0
+    container_name: es
+    environment:
+      - node.name=es
+      - cluster.name=es-docker-cluster
+      - discovery.seed_hosts=es
+      - cluster.initial_master_nodes=es
+      - bootstrap.memory_lock=true
+      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
+    ulimits:
+      memlock:
+        soft: -1
+        hard: -1
+    volumes:
+      - es_data:/usr/share/elasticsearch/data
+    ports:
+      - 9200:9200
+    networks:
+      - elastic
+
+  kibana:
+    image: docker.elastic.co/kibana/kibana:7.9.0
+    container_name: kibana
+    ports:
+      - 5601:5601
+    environment:
+      ELASTICSEARCH_URL: http://es:9200
+      ELASTICSEARCH_HOSTS: http://es:9200
+    networks:
+      - elastic
+
+volumes:
+  es_data:
+    driver: local
+
+networks:
+  elastic:
+    driver: bridge

kibana/README.md

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+# elasticsearch-sandbox kibana
+
+
+Vamos utilizar o kibana para rodar todos os comandos de criação de indices, para criar os documentos e buscar os mesmos.
+

kibana/example1.md

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+# elasticsearch-sandbox kibana example 1
+
+Vamos seguir os passos do artigo https://www.rtancman.com.br/information-retrieval/elasticsearch-como-ferramenta-de-busca.html#executando-comandos-do-elasticsearch

kibana/example2.md

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+# elasticsearch-sandbox kibana example 2
+
+Vamos ver na prática como os analyzers e os tokenizers funcionam.
+
+```
+PUT /example2
+{
+  "settings": {
+    "index": {
+      "analysis": {
+        "filter": {
+          "synonym": {
+            "type": "synonym",
+            "lenient": true,
+            "synonyms": [
+              "i-pod, i pod => ipod",
+              "universe, cosmos"
+            ]
+          }
+        },
+        "analyzer": {
+          "default": {
+            "filter": [
+              "asciifolding",
+              "lowercase",
+              "stop",
+              "synonym"
+            ],
+            "tokenizer": "standard"
+          }
+        }
+      }
+    }
+  }
+}
+
+GET /_analyze
+{
+  "tokenizer": "standard",
+  "filter": [ "stop" ],
+  "text": "a quick fox jumps over the lazy dog"
+}
+
+POST /example2/_analyze
+{
+  "analyzer": "default",
+  "text": "Este é um exemplo de texto que eu gostaria de indexar."
+}
+
+POST /example2/_analyze
+{
+  "analyzer": "default",
+  "text": "i pod e cosmos são coisas bem loucas!"
+}
+
+
+POST /example2/_doc
+{
+  "title":"Este é um exemplo de texto que eu gostaria de indexar."
+}
+
+POST /example2/_doc
+{
+  "title":"exemplo para comprar um i pod."
+}
+
+GET /example2/_search
+{
+  "query": {
+    "query_string": {
+        "query":"exemplo de texto",
+        "analyzer": "default"
+    }
+  }
+}
+```

python/README.md

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+# elasticsearch-sandbox python
+
+
+Vamos utilizar o cliente python oficial do elasticsearch que é o [elastic/elasticsearch-py](https://github.com/elastic/elasticsearch-py). 
+
+O link da documentação é o [https://elasticsearch-py.readthedocs.io/en/master/index.html](https://elasticsearch-py.readthedocs.io/en/master/index.html).
+

python/example1.py

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+from datetime import datetime
+from elasticsearch import Elasticsearch
+es = Elasticsearch()
+
+doc = {
+    'author': 'kimchy',
+    'text': 'Elasticsearch: cool. bonsai cool.',
+    'timestamp': datetime.now(),
+}
+res = es.index(index="test-index", id=1, body=doc)
+print(res['result'])
+
+res = es.get(index="test-index", id=1)
+print(res['_source'])
+
+es.indices.refresh(index="test-index")
+
+res = es.search(index="test-index", body={"query": {"match_all": {}}})
+print("Got %d Hits:" % res['hits']['total']['value'])
+for hit in res['hits']['hits']:
+    print("%(timestamp)s %(author)s: %(text)s" % hit["_source"])