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Copy pathcontext.rs
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1185 lines (1053 loc) · 41.1 KB
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// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.
use std::collections::{HashMap, HashSet};
use std::path::PathBuf;
use std::str::FromStr;
use std::sync::Arc;
use arrow::array::RecordBatchReader;
use arrow::ffi_stream::ArrowArrayStreamReader;
use arrow::pyarrow::FromPyArrow;
use datafusion::execution::session_state::SessionStateBuilder;
use object_store::ObjectStore;
use url::Url;
use uuid::Uuid;
use pyo3::exceptions::{PyKeyError, PyValueError};
use pyo3::prelude::*;
use crate::catalog::{PyCatalog, RustWrappedPyCatalogProvider};
use crate::dataframe::PyDataFrame;
use crate::dataset::Dataset;
use crate::errors::{py_datafusion_err, to_datafusion_err, PyDataFusionResult};
use crate::expr::sort_expr::PySortExpr;
use crate::physical_plan::PyExecutionPlan;
use crate::record_batch::PyRecordBatchStream;
use crate::sql::exceptions::py_value_err;
use crate::sql::logical::PyLogicalPlan;
use crate::store::StorageContexts;
use crate::table::PyTable;
use crate::udaf::PyAggregateUDF;
use crate::udf::PyScalarUDF;
use crate::udtf::PyTableFunction;
use crate::udwf::PyWindowUDF;
use crate::utils::{get_global_ctx, get_tokio_runtime, validate_pycapsule, wait_for_future};
use datafusion::arrow::datatypes::{DataType, Schema, SchemaRef};
use datafusion::arrow::pyarrow::PyArrowType;
use datafusion::arrow::record_batch::RecordBatch;
use datafusion::catalog::CatalogProvider;
use datafusion::common::TableReference;
use datafusion::common::{exec_err, ScalarValue};
use datafusion::datasource::file_format::file_compression_type::FileCompressionType;
use datafusion::datasource::file_format::parquet::ParquetFormat;
use datafusion::datasource::listing::{
ListingOptions, ListingTable, ListingTableConfig, ListingTableUrl,
};
use datafusion::datasource::MemTable;
use datafusion::datasource::TableProvider;
use datafusion::execution::context::{
DataFilePaths, SQLOptions, SessionConfig, SessionContext, TaskContext,
};
use datafusion::execution::disk_manager::DiskManagerMode;
use datafusion::execution::memory_pool::{FairSpillPool, GreedyMemoryPool, UnboundedMemoryPool};
use datafusion::execution::options::ReadOptions;
use datafusion::execution::runtime_env::RuntimeEnvBuilder;
use datafusion::physical_plan::SendableRecordBatchStream;
use datafusion::prelude::{
AvroReadOptions, CsvReadOptions, DataFrame, NdJsonReadOptions, ParquetReadOptions,
};
use datafusion_ffi::catalog_provider::{FFI_CatalogProvider, ForeignCatalogProvider};
use pyo3::types::{PyCapsule, PyDict, PyList, PyTuple, PyType};
use pyo3::IntoPyObjectExt;
use tokio::task::JoinHandle;
/// Configuration options for a SessionContext
#[pyclass(frozen, name = "SessionConfig", module = "datafusion", subclass)]
#[derive(Clone, Default)]
pub struct PySessionConfig {
pub config: SessionConfig,
}
impl From<SessionConfig> for PySessionConfig {
fn from(config: SessionConfig) -> Self {
Self { config }
}
}
#[pymethods]
impl PySessionConfig {
#[pyo3(signature = (config_options=None))]
#[new]
fn new(config_options: Option<HashMap<String, String>>) -> Self {
let mut config = SessionConfig::new();
if let Some(hash_map) = config_options {
for (k, v) in &hash_map {
config = config.set(k, &ScalarValue::Utf8(Some(v.clone())));
}
}
Self { config }
}
fn with_create_default_catalog_and_schema(&self, enabled: bool) -> Self {
Self::from(
self.config
.clone()
.with_create_default_catalog_and_schema(enabled),
)
}
fn with_default_catalog_and_schema(&self, catalog: &str, schema: &str) -> Self {
Self::from(
self.config
.clone()
.with_default_catalog_and_schema(catalog, schema),
)
}
fn with_information_schema(&self, enabled: bool) -> Self {
Self::from(self.config.clone().with_information_schema(enabled))
}
fn with_batch_size(&self, batch_size: usize) -> Self {
Self::from(self.config.clone().with_batch_size(batch_size))
}
fn with_target_partitions(&self, target_partitions: usize) -> Self {
Self::from(
self.config
.clone()
.with_target_partitions(target_partitions),
)
}
fn with_repartition_aggregations(&self, enabled: bool) -> Self {
Self::from(self.config.clone().with_repartition_aggregations(enabled))
}
fn with_repartition_joins(&self, enabled: bool) -> Self {
Self::from(self.config.clone().with_repartition_joins(enabled))
}
fn with_repartition_windows(&self, enabled: bool) -> Self {
Self::from(self.config.clone().with_repartition_windows(enabled))
}
fn with_repartition_sorts(&self, enabled: bool) -> Self {
Self::from(self.config.clone().with_repartition_sorts(enabled))
}
fn with_repartition_file_scans(&self, enabled: bool) -> Self {
Self::from(self.config.clone().with_repartition_file_scans(enabled))
}
fn with_repartition_file_min_size(&self, size: usize) -> Self {
Self::from(self.config.clone().with_repartition_file_min_size(size))
}
fn with_parquet_pruning(&self, enabled: bool) -> Self {
Self::from(self.config.clone().with_parquet_pruning(enabled))
}
fn set(&self, key: &str, value: &str) -> Self {
Self::from(self.config.clone().set_str(key, value))
}
}
/// Runtime options for a SessionContext
#[pyclass(frozen, name = "RuntimeEnvBuilder", module = "datafusion", subclass)]
#[derive(Clone)]
pub struct PyRuntimeEnvBuilder {
pub builder: RuntimeEnvBuilder,
}
#[pymethods]
impl PyRuntimeEnvBuilder {
#[new]
fn new() -> Self {
Self {
builder: RuntimeEnvBuilder::default(),
}
}
fn with_disk_manager_disabled(&self) -> Self {
let mut runtime_builder = self.builder.clone();
let mut disk_mgr_builder = runtime_builder
.disk_manager_builder
.clone()
.unwrap_or_default();
disk_mgr_builder.set_mode(DiskManagerMode::Disabled);
runtime_builder = runtime_builder.with_disk_manager_builder(disk_mgr_builder);
Self {
builder: runtime_builder,
}
}
fn with_disk_manager_os(&self) -> Self {
let mut runtime_builder = self.builder.clone();
let mut disk_mgr_builder = runtime_builder
.disk_manager_builder
.clone()
.unwrap_or_default();
disk_mgr_builder.set_mode(DiskManagerMode::OsTmpDirectory);
runtime_builder = runtime_builder.with_disk_manager_builder(disk_mgr_builder);
Self {
builder: runtime_builder,
}
}
fn with_disk_manager_specified(&self, paths: Vec<String>) -> Self {
let paths = paths.iter().map(|s| s.into()).collect();
let mut runtime_builder = self.builder.clone();
let mut disk_mgr_builder = runtime_builder
.disk_manager_builder
.clone()
.unwrap_or_default();
disk_mgr_builder.set_mode(DiskManagerMode::Directories(paths));
runtime_builder = runtime_builder.with_disk_manager_builder(disk_mgr_builder);
Self {
builder: runtime_builder,
}
}
fn with_unbounded_memory_pool(&self) -> Self {
let builder = self.builder.clone();
let builder = builder.with_memory_pool(Arc::new(UnboundedMemoryPool::default()));
Self { builder }
}
fn with_fair_spill_pool(&self, size: usize) -> Self {
let builder = self.builder.clone();
let builder = builder.with_memory_pool(Arc::new(FairSpillPool::new(size)));
Self { builder }
}
fn with_greedy_memory_pool(&self, size: usize) -> Self {
let builder = self.builder.clone();
let builder = builder.with_memory_pool(Arc::new(GreedyMemoryPool::new(size)));
Self { builder }
}
fn with_temp_file_path(&self, path: &str) -> Self {
let builder = self.builder.clone();
let builder = builder.with_temp_file_path(path);
Self { builder }
}
}
/// `PySQLOptions` allows you to specify options to the sql execution.
#[pyclass(frozen, name = "SQLOptions", module = "datafusion", subclass)]
#[derive(Clone)]
pub struct PySQLOptions {
pub options: SQLOptions,
}
impl From<SQLOptions> for PySQLOptions {
fn from(options: SQLOptions) -> Self {
Self { options }
}
}
#[pymethods]
impl PySQLOptions {
#[new]
fn new() -> Self {
let options = SQLOptions::new();
Self { options }
}
/// Should DDL data modification commands (e.g. `CREATE TABLE`) be run? Defaults to `true`.
fn with_allow_ddl(&self, allow: bool) -> Self {
Self::from(self.options.with_allow_ddl(allow))
}
/// Should DML data modification commands (e.g. `INSERT and COPY`) be run? Defaults to `true`
pub fn with_allow_dml(&self, allow: bool) -> Self {
Self::from(self.options.with_allow_dml(allow))
}
/// Should Statements such as (e.g. `SET VARIABLE and `BEGIN TRANSACTION` ...`) be run?. Defaults to `true`
pub fn with_allow_statements(&self, allow: bool) -> Self {
Self::from(self.options.with_allow_statements(allow))
}
}
/// `PySessionContext` is able to plan and execute DataFusion plans.
/// It has a powerful optimizer, a physical planner for local execution, and a
/// multi-threaded execution engine to perform the execution.
#[pyclass(frozen, name = "SessionContext", module = "datafusion", subclass)]
#[derive(Clone)]
pub struct PySessionContext {
pub ctx: SessionContext,
}
#[pymethods]
impl PySessionContext {
#[pyo3(signature = (config=None, runtime=None))]
#[new]
pub fn new(
config: Option<PySessionConfig>,
runtime: Option<PyRuntimeEnvBuilder>,
) -> PyDataFusionResult<Self> {
let config = if let Some(c) = config {
c.config
} else {
SessionConfig::default().with_information_schema(true)
};
let runtime_env_builder = if let Some(c) = runtime {
c.builder
} else {
RuntimeEnvBuilder::default()
};
let runtime = Arc::new(runtime_env_builder.build()?);
let session_state = SessionStateBuilder::new()
.with_config(config)
.with_runtime_env(runtime)
.with_default_features()
.build();
Ok(PySessionContext {
ctx: SessionContext::new_with_state(session_state),
})
}
pub fn enable_url_table(&self) -> PyResult<Self> {
Ok(PySessionContext {
ctx: self.ctx.clone().enable_url_table(),
})
}
#[classmethod]
#[pyo3(signature = ())]
fn global_ctx(_cls: &Bound<'_, PyType>) -> PyResult<Self> {
Ok(Self {
ctx: get_global_ctx().clone(),
})
}
/// Register an object store with the given name
#[pyo3(signature = (scheme, store, host=None))]
pub fn register_object_store(
&self,
scheme: &str,
store: StorageContexts,
host: Option<&str>,
) -> PyResult<()> {
// for most stores the "host" is the bucket name and can be inferred from the store
let (store, upstream_host): (Arc<dyn ObjectStore>, String) = match store {
StorageContexts::AmazonS3(s3) => (s3.inner, s3.bucket_name),
StorageContexts::GoogleCloudStorage(gcs) => (gcs.inner, gcs.bucket_name),
StorageContexts::MicrosoftAzure(azure) => (azure.inner, azure.container_name),
StorageContexts::LocalFileSystem(local) => (local.inner, "".to_string()),
StorageContexts::HTTP(http) => (http.store, http.url),
};
// let users override the host to match the api signature from upstream
let derived_host = if let Some(host) = host {
host
} else {
&upstream_host
};
let url_string = format!("{scheme}{derived_host}");
let url = Url::parse(&url_string).unwrap();
self.ctx.runtime_env().register_object_store(&url, store);
Ok(())
}
#[allow(clippy::too_many_arguments)]
#[pyo3(signature = (name, path, table_partition_cols=vec![],
file_extension=".parquet",
schema=None,
file_sort_order=None))]
pub fn register_listing_table(
&self,
name: &str,
path: &str,
table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
file_extension: &str,
schema: Option<PyArrowType<Schema>>,
file_sort_order: Option<Vec<Vec<PySortExpr>>>,
py: Python,
) -> PyDataFusionResult<()> {
let options = ListingOptions::new(Arc::new(ParquetFormat::new()))
.with_file_extension(file_extension)
.with_table_partition_cols(
table_partition_cols
.into_iter()
.map(|(name, ty)| (name, ty.0))
.collect::<Vec<(String, DataType)>>(),
)
.with_file_sort_order(
file_sort_order
.unwrap_or_default()
.into_iter()
.map(|e| e.into_iter().map(|f| f.into()).collect())
.collect(),
);
let table_path = ListingTableUrl::parse(path)?;
let resolved_schema: SchemaRef = match schema {
Some(s) => Arc::new(s.0),
None => {
let state = self.ctx.state();
let schema = options.infer_schema(&state, &table_path);
wait_for_future(py, schema)??
}
};
let config = ListingTableConfig::new(table_path)
.with_listing_options(options)
.with_schema(resolved_schema);
let table = ListingTable::try_new(config)?;
self.ctx.register_table(name, Arc::new(table))?;
Ok(())
}
pub fn register_udtf(&self, func: PyTableFunction) {
let name = func.name.clone();
let func = Arc::new(func);
self.ctx.register_udtf(&name, func);
}
/// Returns a PyDataFrame whose plan corresponds to the SQL statement.
pub fn sql(&self, query: &str, py: Python) -> PyDataFusionResult<PyDataFrame> {
let result = self.ctx.sql(query);
let df = wait_for_future(py, result)??;
Ok(PyDataFrame::new(df))
}
#[pyo3(signature = (query, options=None))]
pub fn sql_with_options(
&self,
query: &str,
options: Option<PySQLOptions>,
py: Python,
) -> PyDataFusionResult<PyDataFrame> {
let options = if let Some(options) = options {
options.options
} else {
SQLOptions::new()
};
let result = self.ctx.sql_with_options(query, options);
let df = wait_for_future(py, result)??;
Ok(PyDataFrame::new(df))
}
#[pyo3(signature = (partitions, name=None, schema=None))]
pub fn create_dataframe(
&self,
partitions: PyArrowType<Vec<Vec<RecordBatch>>>,
name: Option<&str>,
schema: Option<PyArrowType<Schema>>,
py: Python,
) -> PyDataFusionResult<PyDataFrame> {
let schema = if let Some(schema) = schema {
SchemaRef::from(schema.0)
} else {
partitions.0[0][0].schema()
};
let table = MemTable::try_new(schema, partitions.0)?;
// generate a random (unique) name for this table if none is provided
// table name cannot start with numeric digit
let table_name = match name {
Some(val) => val.to_owned(),
None => {
"c".to_owned()
+ Uuid::new_v4()
.simple()
.encode_lower(&mut Uuid::encode_buffer())
}
};
self.ctx.register_table(&*table_name, Arc::new(table))?;
let table = wait_for_future(py, self._table(&table_name))??;
let df = PyDataFrame::new(table);
Ok(df)
}
/// Create a DataFrame from an existing logical plan
pub fn create_dataframe_from_logical_plan(&self, plan: PyLogicalPlan) -> PyDataFrame {
PyDataFrame::new(DataFrame::new(self.ctx.state(), plan.plan.as_ref().clone()))
}
/// Construct datafusion dataframe from Python list
#[pyo3(signature = (data, name=None))]
pub fn from_pylist(
&self,
data: Bound<'_, PyList>,
name: Option<&str>,
) -> PyResult<PyDataFrame> {
// Acquire GIL Token
let py = data.py();
// Instantiate pyarrow Table object & convert to Arrow Table
let table_class = py.import("pyarrow")?.getattr("Table")?;
let args = PyTuple::new(py, &[data])?;
let table = table_class.call_method1("from_pylist", args)?;
// Convert Arrow Table to datafusion DataFrame
let df = self.from_arrow(table, name, py)?;
Ok(df)
}
/// Construct datafusion dataframe from Python dictionary
#[pyo3(signature = (data, name=None))]
pub fn from_pydict(
&self,
data: Bound<'_, PyDict>,
name: Option<&str>,
) -> PyResult<PyDataFrame> {
// Acquire GIL Token
let py = data.py();
// Instantiate pyarrow Table object & convert to Arrow Table
let table_class = py.import("pyarrow")?.getattr("Table")?;
let args = PyTuple::new(py, &[data])?;
let table = table_class.call_method1("from_pydict", args)?;
// Convert Arrow Table to datafusion DataFrame
let df = self.from_arrow(table, name, py)?;
Ok(df)
}
/// Construct datafusion dataframe from Arrow Table
#[pyo3(signature = (data, name=None))]
pub fn from_arrow(
&self,
data: Bound<'_, PyAny>,
name: Option<&str>,
py: Python,
) -> PyDataFusionResult<PyDataFrame> {
let (schema, batches) =
if let Ok(stream_reader) = ArrowArrayStreamReader::from_pyarrow_bound(&data) {
// Works for any object that implements __arrow_c_stream__ in pycapsule.
let schema = stream_reader.schema().as_ref().to_owned();
let batches = stream_reader
.collect::<Result<Vec<RecordBatch>, arrow::error::ArrowError>>()?;
(schema, batches)
} else if let Ok(array) = RecordBatch::from_pyarrow_bound(&data) {
// While this says RecordBatch, it will work for any object that implements
// __arrow_c_array__ and returns a StructArray.
(array.schema().as_ref().to_owned(), vec![array])
} else {
return Err(crate::errors::PyDataFusionError::Common(
"Expected either a Arrow Array or Arrow Stream in from_arrow().".to_string(),
));
};
// Because create_dataframe() expects a vector of vectors of record batches
// here we need to wrap the vector of record batches in an additional vector
let list_of_batches = PyArrowType::from(vec![batches]);
self.create_dataframe(list_of_batches, name, Some(schema.into()), py)
}
/// Construct datafusion dataframe from pandas
#[allow(clippy::wrong_self_convention)]
#[pyo3(signature = (data, name=None))]
pub fn from_pandas(&self, data: Bound<'_, PyAny>, name: Option<&str>) -> PyResult<PyDataFrame> {
// Obtain GIL token
let py = data.py();
// Instantiate pyarrow Table object & convert to Arrow Table
let table_class = py.import("pyarrow")?.getattr("Table")?;
let args = PyTuple::new(py, &[data])?;
let table = table_class.call_method1("from_pandas", args)?;
// Convert Arrow Table to datafusion DataFrame
let df = self.from_arrow(table, name, py)?;
Ok(df)
}
/// Construct datafusion dataframe from polars
#[pyo3(signature = (data, name=None))]
pub fn from_polars(&self, data: Bound<'_, PyAny>, name: Option<&str>) -> PyResult<PyDataFrame> {
// Convert Polars dataframe to Arrow Table
let table = data.call_method0("to_arrow")?;
// Convert Arrow Table to datafusion DataFrame
let df = self.from_arrow(table, name, data.py())?;
Ok(df)
}
pub fn register_table(&self, name: &str, table: Bound<'_, PyAny>) -> PyDataFusionResult<()> {
let table = PyTable::new(&table)?;
self.ctx.register_table(name, table.table)?;
Ok(())
}
pub fn deregister_table(&self, name: &str) -> PyDataFusionResult<()> {
self.ctx.deregister_table(name)?;
Ok(())
}
pub fn register_catalog_provider(
&self,
name: &str,
provider: Bound<'_, PyAny>,
) -> PyDataFusionResult<()> {
let provider = if provider.hasattr("__datafusion_catalog_provider__")? {
let capsule = provider
.getattr("__datafusion_catalog_provider__")?
.call0()?;
let capsule = capsule.downcast::<PyCapsule>().map_err(py_datafusion_err)?;
validate_pycapsule(capsule, "datafusion_catalog_provider")?;
let provider = unsafe { capsule.reference::<FFI_CatalogProvider>() };
let provider: ForeignCatalogProvider = provider.into();
Arc::new(provider) as Arc<dyn CatalogProvider>
} else {
match provider.extract::<PyCatalog>() {
Ok(py_catalog) => py_catalog.catalog,
Err(_) => Arc::new(RustWrappedPyCatalogProvider::new(provider.into()))
as Arc<dyn CatalogProvider>,
}
};
let _ = self.ctx.register_catalog(name, provider);
Ok(())
}
/// Construct datafusion dataframe from Arrow Table
pub fn register_table_provider(
&self,
name: &str,
provider: Bound<'_, PyAny>,
) -> PyDataFusionResult<()> {
// Deprecated: use `register_table` instead
self.register_table(name, provider)
}
pub fn register_record_batches(
&self,
name: &str,
partitions: PyArrowType<Vec<Vec<RecordBatch>>>,
) -> PyDataFusionResult<()> {
let schema = partitions.0[0][0].schema();
let table = MemTable::try_new(schema, partitions.0)?;
self.ctx.register_table(name, Arc::new(table))?;
Ok(())
}
#[allow(clippy::too_many_arguments)]
#[pyo3(signature = (name, path, table_partition_cols=vec![],
parquet_pruning=true,
file_extension=".parquet",
skip_metadata=true,
schema=None,
file_sort_order=None))]
pub fn register_parquet(
&self,
name: &str,
path: &str,
table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
parquet_pruning: bool,
file_extension: &str,
skip_metadata: bool,
schema: Option<PyArrowType<Schema>>,
file_sort_order: Option<Vec<Vec<PySortExpr>>>,
py: Python,
) -> PyDataFusionResult<()> {
let mut options = ParquetReadOptions::default()
.table_partition_cols(
table_partition_cols
.into_iter()
.map(|(name, ty)| (name, ty.0))
.collect::<Vec<(String, DataType)>>(),
)
.parquet_pruning(parquet_pruning)
.skip_metadata(skip_metadata);
options.file_extension = file_extension;
options.schema = schema.as_ref().map(|x| &x.0);
options.file_sort_order = file_sort_order
.unwrap_or_default()
.into_iter()
.map(|e| e.into_iter().map(|f| f.into()).collect())
.collect();
let result = self.ctx.register_parquet(name, path, options);
wait_for_future(py, result)??;
Ok(())
}
#[allow(clippy::too_many_arguments)]
#[pyo3(signature = (name,
path,
schema=None,
has_header=true,
delimiter=",",
schema_infer_max_records=1000,
file_extension=".csv",
file_compression_type=None))]
pub fn register_csv(
&self,
name: &str,
path: &Bound<'_, PyAny>,
schema: Option<PyArrowType<Schema>>,
has_header: bool,
delimiter: &str,
schema_infer_max_records: usize,
file_extension: &str,
file_compression_type: Option<String>,
py: Python,
) -> PyDataFusionResult<()> {
let delimiter = delimiter.as_bytes();
if delimiter.len() != 1 {
return Err(crate::errors::PyDataFusionError::PythonError(py_value_err(
"Delimiter must be a single character",
)));
}
let mut options = CsvReadOptions::new()
.has_header(has_header)
.delimiter(delimiter[0])
.schema_infer_max_records(schema_infer_max_records)
.file_extension(file_extension)
.file_compression_type(parse_file_compression_type(file_compression_type)?);
options.schema = schema.as_ref().map(|x| &x.0);
if path.is_instance_of::<PyList>() {
let paths = path.extract::<Vec<String>>()?;
let result = self.register_csv_from_multiple_paths(name, paths, options);
wait_for_future(py, result)??;
} else {
let path = path.extract::<String>()?;
let result = self.ctx.register_csv(name, &path, options);
wait_for_future(py, result)??;
}
Ok(())
}
#[allow(clippy::too_many_arguments)]
#[pyo3(signature = (name,
path,
schema=None,
schema_infer_max_records=1000,
file_extension=".json",
table_partition_cols=vec![],
file_compression_type=None))]
pub fn register_json(
&self,
name: &str,
path: PathBuf,
schema: Option<PyArrowType<Schema>>,
schema_infer_max_records: usize,
file_extension: &str,
table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
file_compression_type: Option<String>,
py: Python,
) -> PyDataFusionResult<()> {
let path = path
.to_str()
.ok_or_else(|| PyValueError::new_err("Unable to convert path to a string"))?;
let mut options = NdJsonReadOptions::default()
.file_compression_type(parse_file_compression_type(file_compression_type)?)
.table_partition_cols(
table_partition_cols
.into_iter()
.map(|(name, ty)| (name, ty.0))
.collect::<Vec<(String, DataType)>>(),
);
options.schema_infer_max_records = schema_infer_max_records;
options.file_extension = file_extension;
options.schema = schema.as_ref().map(|x| &x.0);
let result = self.ctx.register_json(name, path, options);
wait_for_future(py, result)??;
Ok(())
}
#[allow(clippy::too_many_arguments)]
#[pyo3(signature = (name,
path,
schema=None,
file_extension=".avro",
table_partition_cols=vec![]))]
pub fn register_avro(
&self,
name: &str,
path: PathBuf,
schema: Option<PyArrowType<Schema>>,
file_extension: &str,
table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
py: Python,
) -> PyDataFusionResult<()> {
let path = path
.to_str()
.ok_or_else(|| PyValueError::new_err("Unable to convert path to a string"))?;
let mut options = AvroReadOptions::default().table_partition_cols(
table_partition_cols
.into_iter()
.map(|(name, ty)| (name, ty.0))
.collect::<Vec<(String, DataType)>>(),
);
options.file_extension = file_extension;
options.schema = schema.as_ref().map(|x| &x.0);
let result = self.ctx.register_avro(name, path, options);
wait_for_future(py, result)??;
Ok(())
}
// Registers a PyArrow.Dataset
pub fn register_dataset(
&self,
name: &str,
dataset: &Bound<'_, PyAny>,
py: Python,
) -> PyDataFusionResult<()> {
let table: Arc<dyn TableProvider> = Arc::new(Dataset::new(dataset, py)?);
self.ctx.register_table(name, table)?;
Ok(())
}
pub fn register_udf(&self, udf: PyScalarUDF) -> PyResult<()> {
self.ctx.register_udf(udf.function);
Ok(())
}
pub fn register_udaf(&self, udaf: PyAggregateUDF) -> PyResult<()> {
self.ctx.register_udaf(udaf.function);
Ok(())
}
pub fn register_udwf(&self, udwf: PyWindowUDF) -> PyResult<()> {
self.ctx.register_udwf(udwf.function);
Ok(())
}
#[pyo3(signature = (name="datafusion"))]
pub fn catalog(&self, name: &str) -> PyResult<PyObject> {
let catalog = self.ctx.catalog(name).ok_or(PyKeyError::new_err(format!(
"Catalog with name {name} doesn't exist."
)))?;
Python::with_gil(|py| {
match catalog
.as_any()
.downcast_ref::<RustWrappedPyCatalogProvider>()
{
Some(wrapped_schema) => Ok(wrapped_schema.catalog_provider.clone_ref(py)),
None => PyCatalog::from(catalog).into_py_any(py),
}
})
}
pub fn catalog_names(&self) -> HashSet<String> {
self.ctx.catalog_names().into_iter().collect()
}
pub fn tables(&self) -> HashSet<String> {
self.ctx
.catalog_names()
.into_iter()
.filter_map(|name| self.ctx.catalog(&name))
.flat_map(move |catalog| {
catalog
.schema_names()
.into_iter()
.filter_map(move |name| catalog.schema(&name))
})
.flat_map(|schema| schema.table_names())
.collect()
}
pub fn table(&self, name: &str, py: Python) -> PyResult<PyDataFrame> {
let res = wait_for_future(py, self.ctx.table(name))
.map_err(|e| PyKeyError::new_err(e.to_string()))?;
match res {
Ok(df) => Ok(PyDataFrame::new(df)),
Err(e) => {
if let datafusion::error::DataFusionError::Plan(msg) = &e {
if msg.contains("No table named") {
return Err(PyKeyError::new_err(msg.to_string()));
}
}
Err(py_datafusion_err(e))
}
}
}
pub fn table_exist(&self, name: &str) -> PyDataFusionResult<bool> {
Ok(self.ctx.table_exist(name)?)
}
pub fn empty_table(&self) -> PyDataFusionResult<PyDataFrame> {
Ok(PyDataFrame::new(self.ctx.read_empty()?))
}
pub fn session_id(&self) -> String {
self.ctx.session_id()
}
#[allow(clippy::too_many_arguments)]
#[pyo3(signature = (path, schema=None, schema_infer_max_records=1000, file_extension=".json", table_partition_cols=vec![], file_compression_type=None))]
pub fn read_json(
&self,
path: PathBuf,
schema: Option<PyArrowType<Schema>>,
schema_infer_max_records: usize,
file_extension: &str,
table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
file_compression_type: Option<String>,
py: Python,
) -> PyDataFusionResult<PyDataFrame> {
let path = path
.to_str()
.ok_or_else(|| PyValueError::new_err("Unable to convert path to a string"))?;
let mut options = NdJsonReadOptions::default()
.table_partition_cols(
table_partition_cols
.into_iter()
.map(|(name, ty)| (name, ty.0))
.collect::<Vec<(String, DataType)>>(),
)
.file_compression_type(parse_file_compression_type(file_compression_type)?);
options.schema_infer_max_records = schema_infer_max_records;
options.file_extension = file_extension;
let df = if let Some(schema) = schema {
options.schema = Some(&schema.0);
let result = self.ctx.read_json(path, options);
wait_for_future(py, result)??
} else {
let result = self.ctx.read_json(path, options);
wait_for_future(py, result)??
};
Ok(PyDataFrame::new(df))
}
#[allow(clippy::too_many_arguments)]
#[pyo3(signature = (
path,
schema=None,
has_header=true,
delimiter=",",
schema_infer_max_records=1000,
file_extension=".csv",
table_partition_cols=vec![],
file_compression_type=None))]
pub fn read_csv(
&self,
path: &Bound<'_, PyAny>,
schema: Option<PyArrowType<Schema>>,
has_header: bool,
delimiter: &str,
schema_infer_max_records: usize,
file_extension: &str,
table_partition_cols: Vec<(String, PyArrowType<DataType>)>,
file_compression_type: Option<String>,
py: Python,
) -> PyDataFusionResult<PyDataFrame> {
let delimiter = delimiter.as_bytes();
if delimiter.len() != 1 {
return Err(crate::errors::PyDataFusionError::PythonError(py_value_err(
"Delimiter must be a single character",
)));
};
let mut options = CsvReadOptions::new()
.has_header(has_header)
.delimiter(delimiter[0])
.schema_infer_max_records(schema_infer_max_records)
.file_extension(file_extension)
.table_partition_cols(
table_partition_cols
.into_iter()
.map(|(name, ty)| (name, ty.0))
.collect::<Vec<(String, DataType)>>(),
)
.file_compression_type(parse_file_compression_type(file_compression_type)?);
options.schema = schema.as_ref().map(|x| &x.0);
if path.is_instance_of::<PyList>() {
let paths = path.extract::<Vec<String>>()?;
let paths = paths.iter().map(|p| p as &str).collect::<Vec<&str>>();
let result = self.ctx.read_csv(paths, options);
let df = PyDataFrame::new(wait_for_future(py, result)??);
Ok(df)
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