#362 MySQL, Memgraph, AlgoraDB, TinkerPop, HyperGraphDB and Neptune Databases in Python
#362 MySQL, Memgraph, AlgoraDB, TinkerPop, HyperGraphDB and Neptune Databases in Python
#362 Provide sample python code to create a MySQL database, insert, read, update and delete database records.
Here is sample Python code that demonstrates how to create a MySQL database, insert records, read records, update records, and delete records:
import mysql.connector
# Connect to the database
cnx = mysql.connector.connect(user='root', password='password', host='127.0.0.1', database='testdb')
cursor = cnx.cursor()
# Create a table
cursor.execute("CREATE TABLE employees (id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(255), salary INT)")
# Insert a record into the table
cursor.execute("INSERT INTO employees (name, salary) VALUES ('John Doe', 50000)")
cnx.commit()
# Read records from the table
cursor.execute("SELECT * FROM employees")
results = cursor.fetchall()
for row in results:
print(row)
# Update a record in the table
cursor.execute("UPDATE employees SET salary = 55000 WHERE name = 'John Doe'")
cnx.commit()
# Delete a record from the table
cursor.execute("DELETE FROM employees WHERE name = 'John Doe'")
cnx.commit()
# Close the connection
cnx.close()
Please note that above code is just an example, you need to make sure you have the mysql connector library installed, and make sure you have the correct credentials to connect to your specific database.
You also need to create the database 'testdb' before running the above code snippet.
Also, it is highly recommended to use try-except block to handle exceptions, so that if an error occurs your application doesn't crash.
try:
# Connect to the database
cnx = mysql.connector.connect(user='root', password='password', host='127.0.0.1', database='testdb')
cursor = cnx.cursor()
# Create a table
cursor.execute("CREATE TABLE employees (id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(255), salary INT)")
# Insert a record into the table
cursor.execute("INSERT INTO employees (name, salary) VALUES ('John Doe', 50000)")
cnx.commit()
# Read records from the table
cursor.execute("SELECT * FROM employees")
results = cursor.fetchall()
for row in results:
print(row)
# Update a record in the table
cursor.execute("UPDATE employees SET salary = 55000 WHERE name = 'John Doe'")
cnx.commit()
# Delete a record from the table
cursor.execute("DELETE FROM employees WHERE name = 'John Doe'")
cnx.commit()
except mysql.connector.Error as err:
print(err)
finally:
# Close the connection
cnx.close()
Provide sample python code to create a Memgraph database, insert, read, update and delete database records.
Here is sample Python code that demonstrates how to create a Memgraph database, insert records, read records, update records, and delete records using the Memgraph python driver:
import memgraph
# Connect to the database
connection = memgraph.connect("bolt://localhost:7687", user_name="memgraph", password="password")
# Create a table
connection.run("CREATE (n:Employee) SET n.name='John Doe', n.salary=50000")
# Insert a record into the table
connection.run("CREATE (n:Employee) SET n.name='Jane Smith', n.salary=60000")
# Read records from the table
results = connection.run("MATCH (n:Employee) RETURN n.name, n.salary")
for record in results:
print(record)
# Update a record in the table
connection.run("MATCH (n:Employee) WHERE n.name='John Doe' SET n.salary=55000")
# Delete a record from the table
connection.run("MATCH (n:Employee) WHERE n.name='Jane Smith' DELETE n")
# Close the connection
connection.close()
Please note that above code is just an example, you need to make sure you have the memgraph python driver library installed, and make sure you have the correct credentials to connect to your specific database.
Also, it is highly recommended to use try-except block to handle exceptions, so that if an error occurs your application doesn't crash.
try:
# Connect to the database
connection = memgraph.connect("bolt://localhost:7687", user_name="memgraph", password="password")
# Create a table
connection.run("CREATE (n:Employee) SET n.name='John Doe', n.salary=50000")
# Insert a record into the table
connection.run("CREATE (n:Employee) SET n.name='Jane Smith', n.salary=60000")
# Read records from the table
results = connection.run("MATCH (n:Employee) RETURN n.name, n.salary")
for record in results:
print(record)
# Update a record in the table
connection.run("MATCH (n:Employee) WHERE n.name='John Doe' SET n.salary=55000")
# Delete a record from the table
connection.run("MATCH (n:Employee) WHERE n.name='Jane Smith' DELETE n")
except Exception as err:
print(err)
finally:
# Close the connection
connection.close()
Please note that Memgraph database is a graph database, and the Cypher query language it used to manipulate the data. It has a different syntax for creating, inserting, reading, updating and deleting records than traditional relational databases.
Provide sample python code to create a Arango database, insert, read, update and delete database records.
Here is sample Python code that demonstrates how to create an ArangoDB database, insert records, read records, update records, and delete records using the python-arango driver:
from arango import ArangoClient
# Initialize the client for ArangoDB
client = ArangoClient()
# Connect to the "testdb" database as "root" user
db = client.db('testdb', username='root', password='password')
# Create a collection "employees"
db.create_collection('employees')
# Insert a record into the collection
employee = {"name": "John Doe", "salary": 50000}
db.collection('employees').insert(employee)
# Read records from the collection
cursor = db.aql.execute('FOR employee IN employees RETURN employee')
for doc in cursor:
print(doc)
# Update a record in the collection
db.aql.execute("FOR employee IN employees FILTER employee.name == 'John Doe' UPDATE employee WITH { 'salary': 55000 } IN employees")
# Delete a record from the collection
db.aql.execute("FOR employee IN employees FILTER employee.name == 'John Doe' REMOVE employee IN employees")
Please note that above code is just an example, you need to make sure you have the python-arango library installed, and make sure you have the correct credentials to connect to your specific database.
Also, it is highly recommended to use try-except block to handle exceptions, so that if an error occurs your application doesn't crash.
try:
# Initialize the client for ArangoDB
client = ArangoClient()
# Connect to the "testdb" database as "root" user
db = client.db('testdb', username='root', password='password')
# Create a collection "employees"
db.create_collection('employees')
# Insert a record into the collection
employee = {"name": "John Doe", "salary": 50000}
db.collection('employees').insert(employee)
# Read records from the collection
cursor = db.aql.execute('FOR employee IN employees RETURN employee')
for doc in cursor:
print(doc)
# Update a record in the collection
db.aql.execute("FOR employee IN employees FILTER employee.name == 'John Doe' UPDATE employee WITH { 'salary': 55000 } IN employees")
# Delete a record from the collection
db.aql.execute("FOR employee IN employees FILTER employee.name == 'John Doe' REMOVE employee IN employees")
except Exception as err:
print(err)
Please note that ArangoDB is a multi-model database, it support graph, document, and key-value data models. The AQL query language is used to manipulate the data. It has a different syntax for creating, inserting, reading, updating and deleting records than traditional relational databases.
Provide sample python code to create a HyperGraphDB database, insert, read, update and delete database records.
HyperGraphDB is a Java-based graph database, which means it can be embedded into a JVM-based application, such as a Java or Scala application. However, it also provides a remote API that allows to access the database from other languages such as Python.
Here is sample Python code that demonstrates how to create a HyperGraphDB database, insert records, read records, update records, and delete records using the hypergraphdb-client library:
import hypergraphdb as hg
# Connect to the database
hgclient = hg.client("http://localhost:8080")
# Create a new graph
hgclient.create_graph("mygraph")
# Insert a vertex into the graph
hgclient.add_vertex("mygraph", {"name": "John Doe", "salary": 50000})
# Read records from the graph
cursor = hgclient.query("mygraph", "g.V()")
for vertex in cursor:
print(vertex)
# Update a vertex in the graph
hgclient.update_vertex("mygraph", {"name": "John Doe", "salary": 55000})
# Delete a vertex from the graph
hgclient.delete_vertex("mygraph", {"name": "John Doe"})
# Close the connection
hgclient.close()
Please note that above code is just an example, you need to make sure you have the hypergraphdb-client library installed, and make sure you have the correct credentials to connect to your specific database.
Also, it is highly recommended to use try-except block to handle exceptions, so that if an error occurs your application doesn't crash.
try:
# Connect to the database
hgclient = hg.client("http://localhost:8080")
# Create a new graph
hgclient.create_graph("mygraph")
# Insert a vertex into the graph
hgclient.add_vertex("mygraph", {"name": "John Doe", "salary": 50000})
# Read records from the graph
cursor = hgclient.query("mygraph", "g.V()")
for vertex in cursor:
print(vertex)
# Update a vertex in the graph
hgclient.update_vertex("mygraph", {"name": "John Doe", "salary": 55000})
# Delete a vertex from the graph
hgclient.delete_vertex("mygraph", {"name": "John Doe"})
except Exception as err:
print(err)
finally:
# Close the connection
hgclient.close()
Please note that HyperGraphDB is a graph database and it use a query language similar to Gremlin, which is used to manipulate the data. It has a different syntax for creating, inserting, reading, updating and deleting records than traditional relational databases.
Provide sample python code to create a TinkerPop database, insert, read, update and delete database records.
TinkerPop is a graph computing framework that provides a set of libraries and tools to build graph-based systems. It can be used with several graph databases such as Apache JanusGraph, Apache TinkerGraph, and Neo4j, to name a few.
Here is sample Python code that demonstrates how to create a TinkerPop database using Neo4j as the underlying graph database, insert records, read records, update records, and delete records using the gremlinpython library:
from gremlin_python import statics
from gremlin_python.structure.graph import Graph
from gremlin_python.process.graph_traversal import __
from gremlin_python.process.strategies import *
from gremlin_python.driver.driver_remote_connection import DriverRemoteConnection
# Connect to the Neo4j database
graph = Graph().traversal().withRemote(DriverRemoteConnection('ws://localhost:8182/gremlin','g'))
# Insert a vertex into the graph
graph.addV("employee").property("name", "John Doe").property("salary", 50000).next()
# Read records from the graph
cursor = graph.V().hasLabel("employee")
for vertex in cursor:
print(vertex)
# Update a vertex in the graph
graph.V().has("name", "John Doe").property("salary", 55000).next()
# Delete a vertex from the graph
graph.V().has("name", "John Doe").drop().iterate()
# Close the connection
graph.close()
Please note that above code is just an example, you need to make sure you have the gremlinpython library installed, and make sure you have the correct credentials to connect to your specific database.
Also, it is highly recommended to use try-except block to handle exceptions, so that if an error occurs your application doesn't crash.
try:
# Connect to the Neo4j database
graph = Graph().traversal().withRemote(DriverRemoteConnection('ws://localhost:8182/gremlin','g'))
# Insert a vertex into the graph
graph.addV("employee").property("name", "John Doe").property("salary", 50000).next()
# Read records from the graph
cursor = graph.V().hasLabel("employee")
for vertex in cursor:
print(vertex)
# Update a vertex in the graph
graph.V().has("name", "John Doe").property("salary", 55000).next()
# Delete a vertex from the graph
graph.V().has("name", "John Doe").drop().iterate()
except Exception as err:
print(err)
finally:
# Close the connection
graph.close()
Please note that TinkerPop can be used with several graph databases, the above code is an example of how to interact with Neo4j as the underlying graph database. The Gremlin query language is used to manipulate the data. It has a different syntax for creating, inserting, reading, updating and deleting records than traditional relational databases.
Provide sample python code to create a Neptune graph database, insert, read, update and delete database records.
Amazon Neptune is a fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The service supports popular graph models, including property graph and RDF, and allows you to easily build queries that efficiently navigate highly connected datasets.
Here is sample Python code that demonstrates how to create a Neptune graph database, insert records, read records, update records, and delete records using the boto3 library:
import boto3
# Connect to the Neptune client
neptune = boto3.client('neptune')
# Insert a vertex into the graph
neptune.execute_statement(
database='mydb',
resourceArn='arn:aws:neptune:us-west-2:1234567890:db/mydb',
secretArn='arn:aws:secretsmanager:us-west-2:1234567890:secret:mysecret',
statement='CREATE (n:employee {name:"John Doe", salary:50000})'
)
# Read records from the graph
result = neptune.execute_statement(
database='mydb',
resourceArn='arn:aws:neptune:us-west-2:1234567890:db/mydb',
secretArn='arn:aws:secretsmanager:us-west-2:1234567890:secret:mysecret',
statement='MATCH (n:employee) RETURN n.name, n.salary'
)
for record in result["records"]:
print(record)
# Update a vertex in the graph
neptune.execute_statement(
database='mydb',
resourceArn='arn:aws:neptune:us-west-2:1234567890:db/mydb',
secretArn='arn:aws:secretsmanager:us-west-2:1234567890:secret:mysecret',
statement="MATCH (n:employee {name:'John Doe'}) SET n.salary = 55000"
)
Delete a vertex from the graph
neptune.execute_statement(
database='mydb',
resourceArn='arn:aws:neptune:us-west-2:1234567890:db/mydb',
secretArn='arn:aws:secretsmanager:us-west-2:1234567890:secret:mysecret',
statement="MATCH (n:employee {name:'John Doe'}) DELETE n"
)
Please note that above code is just an example, you need to make sure you have the boto3 library installed, and make sure you have the correct credentials to connect to your specific database.
Also, it is highly recommended to use `try-except` block to handle exceptions, so that if an error occurs your application doesn't crash.
try:
# Connect to the Neptune client
neptune = boto3.client('neptune')
# Insert a vertex into the graph
neptune.execute_statement(
database='mydb',
resourceArn='arn:aws:neptune:us-west-2:1234567890:db/mydb',
secretArn='arn:aws:secretsmanager:us-west-2:1234567890:secret:mysecret',
statement='CREATE (n:employee {name:"John Doe", salary:50000})'
)
# Read records from the graph
result = neptune.execute_statement(
database='mydb',
resourceArn='arn:aws:neptune:us-west-2:1234567890:db/mydb',
secretArn='arn:aws:secretsmanager:us-west-2:1234567890:secret:mysecret',
statement='MATCH (n:employee) RETURN n.name, n.salary'
)
for record in result["records"]:
print(record)
# Update a vertex in the graph
neptune.execute_statement(
database='mydb',
resourceArn='arn:aws:neptune:us-west-2:1234567890:db/mydb',
secretArn='arn:aws:secretsmanager:us-west-2:1234567890:secret:mysecret',
statement="MATCH (n:employee {name:'John Doe'}) SET n.salary = 55000"
)
# Delete a vertex from the graph
neptune.execute_statement(
database='mydb',
resourceArn='arn:aws:neptune:us-west-2:1234567890:db/mydb',
secretArn='arn:aws:secretsmanager:us-west-2:1234567890:secret:mysecret',
statement="MATCH (n:employee {name:'John Doe'}) DELETE n"
)
except Exception as err:
print(err)
Please note that Amazon Neptune supports the Gremlin traversal language and Cypher query language to manipulate the data, it has a different syntax for creating, inserting, reading, updating and deleting records than traditional relational databases. Also, the above code is using the `execute_statement` method of the AWS SDK for Python (Boto3) to send Gremlin traversals or Cypher queries to the database, and it requires the ARN of the database and the ARN of the secret that contains the credentials to connect to the database.
It's worth noting that, as with any database, it's recommended to handle the connection and credentials securely and to have proper error handling mechanisms in place when running your code in production environments.
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