Métodos de fábrica do DataStore

from chdb.datastore import DataStore

# Arquivos locais
ds = DataStore.uri("data.csv")
ds = DataStore.uri("/path/to/data.parquet")

# Armazenamento em nuvem
ds = DataStore.uri("s3://bucket/data.parquet?nosign=true")
ds = DataStore.uri("https://example.com/data.csv")

# Bancos de dados
ds = DataStore.uri("mysql://user:pass@host:3306/db/table")
ds = DataStore.uri("postgresql://user:pass@host:5432/db/table")

DataStore.from_file(path, format=None, compression=None, **kwargs)

from chdb.datastore import DataStore

# Detectar formato automaticamente pela extensão
ds = DataStore.from_file("data.csv")
ds = DataStore.from_file("data.parquet")
ds = DataStore.from_file("data.json")

# Formato explícito
ds = DataStore.from_file("data.txt", format="CSV")

# Com compressão
ds = DataStore.from_file("data.csv.gz", compression="gzip")

from chdb import datastore as pd

# Arquivos CSV
ds = pd.read_csv("data.csv")
ds = pd.read_csv("data.csv", sep=";", header=0, nrows=1000)

# Arquivos Parquet (recomendado para grandes conjuntos de dados)
ds = pd.read_parquet("data.parquet")
ds = pd.read_parquet("data.parquet", columns=['col1', 'col2'])

# Arquivos JSON
ds = pd.read_json("data.json")
ds = pd.read_json("data.jsonl", lines=True)

# Arquivos Excel
ds = pd.read_excel("data.xlsx", sheet_name="Sheet1")

DataStore.from_s3(url, access_key_id=None, secret_access_key=None, format=None, **kwargs)

from chdb.datastore import DataStore

# Acesso anônimo (bucket público)
ds = DataStore.from_s3("s3://bucket/data.parquet")

# Com credenciais
ds = DataStore.from_s3(
    "s3://bucket/data.parquet",
    access_key_id="AKIAIOSFODNN7EXAMPLE",
    secret_access_key="wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY"
)

# Usando URI com parâmetros de consulta
ds = DataStore.uri("s3://bucket/data.parquet?nosign=true")
ds = DataStore.uri("s3://bucket/data.parquet?access_key_id=KEY&secret_access_key=SECRET")

DataStore.from_gcs(url, credentials_path=None, **kwargs)

ds = DataStore.from_gcs("gs://bucket/data.parquet")
ds = DataStore.from_gcs("gs://bucket/data.parquet", credentials_path="/path/to/creds.json")

DataStore.from_azure(url, account_name=None, account_key=None, **kwargs)

ds = DataStore.from_azure(
    "az://container/data.parquet",
    account_name="myaccount",
    account_key="mykey"
)

DataStore.from_hdfs(url, **kwargs)

ds = DataStore.from_hdfs("hdfs://namenode:8020/path/data.parquet")

DataStore.from_url(url, format=None, **kwargs)

ds = DataStore.from_url("https://example.com/data.csv")
ds = DataStore.from_url("https://raw.githubusercontent.com/user/repo/main/data.parquet")

DataStore.from_mysql(host, database, table, user, password, port=3306, **kwargs)

ds = DataStore.from_mysql(
    host="localhost",
    database="mydb",
    table="users",
    user="root",
    password="password"
)

# Usando URI
ds = DataStore.uri("mysql://root:password@localhost:3306/mydb/users")

DataStore.from_postgresql(host, database, table, user, password, port=5432, **kwargs)

ds = DataStore.from_postgresql(
    host="localhost",
    database="mydb",
    table="users",
    user="postgres",
    password="password"
)

# Usando URI
ds = DataStore.uri("postgresql://postgres:password@localhost:5432/mydb/users")

DataStore.from_clickhouse(host, database, table, user=None, password=None, port=9000, **kwargs)

ds = DataStore.from_clickhouse(
    host="localhost",
    database="default",
    table="hits",
    user="default",
    password=""
)

# Modo de nível de conexão (explorar bancos de dados)
ds = DataStore.from_clickhouse(
    host="analytics.company.com",
    user="analyst",
    password="secret"
)
ds.databases()                  # Listar bancos de dados
ds.tables("production")         # Listar tabelas
result = ds.sql("SELECT * FROM production.users LIMIT 10")

DataStore.from_mongodb(uri, database, collection, **kwargs)

ds = DataStore.from_mongodb(
    uri="mongodb://localhost:27017",
    database="mydb",
    collection="users"
)

DataStore.from_sqlite(database_path, table, **kwargs)

ds = DataStore.from_sqlite("data.db", table="users")

# Usando URI
ds = DataStore.uri("sqlite:///data.db?table=users")

DataStore.from_iceberg(path, **kwargs)

ds = DataStore.from_iceberg("/path/to/iceberg_table")
ds = DataStore.uri("iceberg://catalog/namespace/table")

DataStore.from_delta(path, **kwargs)

ds = DataStore.from_delta("/path/to/delta_table")
ds = DataStore.uri("deltalake:///path/to/delta_table")

DataStore.from_hudi(path, **kwargs)

ds = DataStore.from_hudi("/path/to/hudi_table")
ds = DataStore.uri("hudi:///path/to/hudi_table")

DataStore.from_df(df, name=None)
DataStore.from_dataframe(df, name=None)  # alias

import pandas

from chdb.datastore import DataStore

pdf = pandas.DataFrame({'a': [1, 2, 3], 'b': ['x', 'y', 'z']})
ds = DataStore.from_df(pdf)

from chdb import datastore as pd

# A partir de dicionário
ds = pd.DataFrame({
    'name': ['Alice', 'Bob'],
    'age': [25, 30]
})

# A partir de um DataFrame do pandas
import pandas

pdf = pandas.DataFrame({'a': [1, 2, 3]})
ds = pd.DataFrame(pdf)

DataStore.from_numbers(count, **kwargs)

ds = DataStore.from_numbers(1000000)  # 1M linhas com coluna 'number'
result = ds.filter(ds['number'] % 2 == 0).head(10)  # Números pares

DataStore.from_random(rows, columns, **kwargs)

ds = DataStore.from_random(rows=1000, columns=5)

DataStore.run_sql(query)

ds = DataStore.run_sql("""
    SELECT number, number * 2 as doubled
    FROM numbers(100)
    WHERE number % 10 = 0
""")