ParquetDataCatalogV2

nautilus_persistence::python::catalog

Struct ParquetDataCatalogV2 

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pub struct ParquetDataCatalogV2 { /* private fields */ }
Expand description

A catalog for writing data to Parquet files.

Implementations§

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impl ParquetDataCatalogV2

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pub fn new( base_path: String, storage_options: Option<HashMap<String, String>>, batch_size: Option<usize>, compression: Option<u8>, max_row_group_size: Option<usize>, ) -> Self

Create a new ParquetCatalog with the given base path and optional parameters.

§Parameters
  • base_path: The base path for the catalog
  • storage_options: Optional storage configuration for cloud backends
  • batch_size: Optional batch size for processing (default: 5000)
  • compression: Optional compression type (0=UNCOMPRESSED, 1=SNAPPY, 2=GZIP, 3=LZO, 4=BROTLI, 5=LZ4, 6=ZSTD)
  • max_row_group_size: Optional maximum row group size (default: 5000)
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pub fn write_quote_ticks( &self, data: Vec<QuoteTick>, start: Option<u64>, end: Option<u64>, skip_disjoint_check: bool, ) -> PyResult<String>

Write quote tick data to Parquet files.

§Parameters
  • data: Vector of quote ticks to write
  • start: Optional start timestamp override (nanoseconds since Unix epoch)
  • end: Optional end timestamp override (nanoseconds since Unix epoch)
§Returns

Returns the path of the created file as a string.

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pub fn write_trade_ticks( &self, data: Vec<TradeTick>, start: Option<u64>, end: Option<u64>, skip_disjoint_check: bool, ) -> PyResult<String>

Write trade tick data to Parquet files.

§Parameters
  • data: Vector of trade ticks to write
  • start: Optional start timestamp override (nanoseconds since Unix epoch)
  • end: Optional end timestamp override (nanoseconds since Unix epoch)
§Returns

Returns the path of the created file as a string.

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pub fn write_order_book_deltas( &self, data: Vec<OrderBookDelta>, start: Option<u64>, end: Option<u64>, skip_disjoint_check: bool, ) -> PyResult<String>

Write order book delta data to Parquet files.

§Parameters
  • data: Vector of order book deltas to write
  • start: Optional start timestamp override (nanoseconds since Unix epoch)
  • end: Optional end timestamp override (nanoseconds since Unix epoch)
§Returns

Returns the path of the created file as a string.

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pub fn write_bars( &self, data: Vec<Bar>, start: Option<u64>, end: Option<u64>, skip_disjoint_check: bool, ) -> PyResult<String>

Write bar data to Parquet files.

§Parameters
  • data: Vector of bars to write
  • start: Optional start timestamp override (nanoseconds since Unix epoch)
  • end: Optional end timestamp override (nanoseconds since Unix epoch)
§Returns

Returns the path of the created file as a string.

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pub fn write_order_book_depths( &self, data: Vec<OrderBookDepth10>, start: Option<u64>, end: Option<u64>, skip_disjoint_check: bool, ) -> PyResult<String>

Write order book depth data to Parquet files.

§Parameters
  • data: Vector of order book depths to write
  • start: Optional start timestamp override (nanoseconds since Unix epoch)
  • end: Optional end timestamp override (nanoseconds since Unix epoch)
§Returns

Returns the path of the created file as a string.

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pub fn write_mark_price_updates( &self, data: Vec<MarkPriceUpdate>, start: Option<u64>, end: Option<u64>, skip_disjoint_check: bool, ) -> PyResult<String>

Write mark price update data to Parquet files.

§Parameters
  • data: Vector of mark price updates to write
  • start: Optional start timestamp override (nanoseconds since Unix epoch)
  • end: Optional end timestamp override (nanoseconds since Unix epoch)
§Returns

Returns the path of the created file as a string.

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pub fn write_index_price_updates( &self, data: Vec<IndexPriceUpdate>, start: Option<u64>, end: Option<u64>, skip_disjoint_check: bool, ) -> PyResult<String>

Write index price update data to Parquet files.

§Parameters
  • data: Vector of index price updates to write
  • start: Optional start timestamp override (nanoseconds since Unix epoch)
  • end: Optional end timestamp override (nanoseconds since Unix epoch)
§Returns

Returns the path of the created file as a string.

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pub fn extend_file_name( &self, data_cls: &str, instrument_id: Option<String>, start: u64, end: u64, ) -> PyResult<()>

Extend file names in the catalog with additional timestamp information.

§Parameters
  • data_cls: The data class name
  • instrument_id: Optional instrument ID filter
  • start: Start timestamp (nanoseconds since Unix epoch)
  • end: End timestamp (nanoseconds since Unix epoch)
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pub fn consolidate_catalog( &self, start: Option<u64>, end: Option<u64>, ensure_contiguous_files: Option<bool>, ) -> PyResult<()>

Consolidate all data files in the catalog within the specified time range.

§Parameters
  • start: Optional start timestamp (nanoseconds since Unix epoch)
  • end: Optional end timestamp (nanoseconds since Unix epoch)
  • ensure_contiguous_files: Optional flag to ensure files are contiguous
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pub fn consolidate_data( &self, type_name: &str, instrument_id: Option<String>, start: Option<u64>, end: Option<u64>, ensure_contiguous_files: Option<bool>, ) -> PyResult<()>

Consolidate data files for a specific data type within the specified time range.

§Parameters
  • type_name: The data type name to consolidate
  • instrument_id: Optional instrument ID filter
  • start: Optional start timestamp (nanoseconds since Unix epoch)
  • end: Optional end timestamp (nanoseconds since Unix epoch)
  • ensure_contiguous_files: Optional flag to ensure files are contiguous
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pub fn consolidate_catalog_by_period( &mut self, period_nanos: Option<u64>, start: Option<u64>, end: Option<u64>, ensure_contiguous_files: Option<bool>, ) -> PyResult<()>

Consolidate all data files in the catalog by splitting them into fixed time periods.

This method identifies all leaf directories in the catalog that contain parquet files and consolidates them by period. A leaf directory is one that contains files but no subdirectories. This is a convenience method that effectively calls consolidate_data_by_period for all data types and instrument IDs in the catalog.

§Parameters
  • period_nanos: Optional period duration for consolidation in nanoseconds. Default is 1 day (86400000000000). Examples: 3600000000000 (1 hour), 604800000000000 (7 days), 1800000000000 (30 minutes)
  • start: Optional start timestamp for the consolidation range (nanoseconds since Unix epoch)
  • end: Optional end timestamp for the consolidation range (nanoseconds since Unix epoch)
  • ensure_contiguous_files: Optional flag to control file naming strategy
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pub fn consolidate_data_by_period( &mut self, type_name: &str, identifier: Option<String>, period_nanos: Option<u64>, start: Option<u64>, end: Option<u64>, ensure_contiguous_files: Option<bool>, ) -> PyResult<()>

Consolidate data files by splitting them into fixed time periods.

This method queries data by period and writes consolidated files immediately, using efficient period-based consolidation logic. When start/end boundaries intersect existing files, the function automatically splits those files to preserve all data.

§Parameters
  • type_name: The data type directory name (e.g., “quotes”, “trades”, “bars”)
  • identifier: Optional instrument ID to consolidate. If None, consolidates all instruments
  • period_nanos: Optional period duration for consolidation in nanoseconds. Default is 1 day (86400000000000). Examples: 3600000000000 (1 hour), 604800000000000 (7 days), 1800000000000 (30 minutes)
  • start: Optional start timestamp for consolidation range (nanoseconds since Unix epoch)
  • end: Optional end timestamp for consolidation range (nanoseconds since Unix epoch)
  • ensure_contiguous_files: Optional flag to control file naming strategy
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pub fn reset_all_file_names(&self) -> PyResult<()>

Reset all catalog file names to their canonical form.

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pub fn reset_data_file_names( &self, data_cls: &str, instrument_id: Option<String>, ) -> PyResult<()>

Reset data file names for a specific data class to their canonical form.

§Parameters
  • data_cls: The data class name
  • instrument_id: Optional instrument ID filter
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pub fn delete_catalog_range( &mut self, start: Option<u64>, end: Option<u64>, ) -> PyResult<()>

Delete data within a specified time range across the entire catalog.

This method identifies all leaf directories in the catalog that contain parquet files and deletes data within the specified time range from each directory. A leaf directory is one that contains files but no subdirectories. This is a convenience method that effectively calls delete_data_range for all data types and instrument IDs in the catalog.

§Parameters
  • start: Optional start timestamp for the deletion range (nanoseconds since Unix epoch)
  • end: Optional end timestamp for the deletion range (nanoseconds since Unix epoch)
§Notes
  • This operation permanently removes data and cannot be undone
  • The deletion process handles file intersections intelligently by splitting files when they partially overlap with the deletion range
  • Files completely within the deletion range are removed entirely
  • Files partially overlapping the deletion range are split to preserve data outside the range
  • This method is useful for bulk data cleanup operations across the entire catalog
  • Empty directories are not automatically removed after deletion
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pub fn delete_data_range( &mut self, type_name: &str, instrument_id: Option<String>, start: Option<u64>, end: Option<u64>, ) -> PyResult<()>

Delete data within a specified time range for a specific data type and instrument.

This method identifies all parquet files that intersect with the specified time range and handles them appropriately:

  • Files completely within the range are deleted
  • Files partially overlapping the range are split to preserve data outside the range
  • The original intersecting files are removed after processing
§Parameters
  • type_name: The data type directory name (e.g., “quotes”, “trades”, “bars”)
  • instrument_id: Optional instrument ID to delete data for. If None, deletes data across all instruments
  • start: Optional start timestamp for the deletion range (nanoseconds since Unix epoch)
  • end: Optional end timestamp for the deletion range (nanoseconds since Unix epoch)
§Notes
  • This operation permanently removes data and cannot be undone
  • Files that partially overlap the deletion range are split to preserve data outside the range
  • The method ensures data integrity by using atomic operations where possible
  • Empty directories are not automatically removed after deletion
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pub fn query_files( &self, data_cls: &str, instrument_ids: Option<Vec<String>>, start: Option<u64>, end: Option<u64>, ) -> PyResult<Vec<String>>

Query files in the catalog matching the specified criteria.

§Parameters
  • data_cls: The data class name to query
  • instrument_ids: Optional list of instrument IDs to filter by
  • start: Optional start timestamp (nanoseconds since Unix epoch)
  • end: Optional end timestamp (nanoseconds since Unix epoch)
§Returns

Returns a list of file paths matching the criteria.

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pub fn get_missing_intervals_for_request( &self, start: u64, end: u64, data_cls: &str, instrument_id: Option<String>, ) -> PyResult<Vec<(u64, u64)>>

Get missing time intervals for a data request.

§Parameters
  • start: Start timestamp (nanoseconds since Unix epoch)
  • end: End timestamp (nanoseconds since Unix epoch)
  • data_cls: The data class name
  • instrument_id: Optional instrument ID filter
§Returns

Returns a list of (start, end) timestamp tuples representing missing intervals.

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pub fn query_last_timestamp( &self, data_cls: &str, instrument_id: Option<String>, ) -> PyResult<Option<u64>>

Query the last timestamp for a specific data class and instrument.

§Parameters
  • data_cls: The data class name
  • instrument_id: Optional instrument ID filter
§Returns

Returns the last timestamp as nanoseconds since Unix epoch, or None if no data exists.

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pub fn get_intervals( &self, data_cls: &str, instrument_id: Option<String>, ) -> PyResult<Vec<(u64, u64)>>

Get time intervals covered by data for a specific data class and instrument.

§Parameters
  • data_cls: The data class name
  • instrument_id: Optional instrument ID filter
§Returns

Returns a list of (start, end) timestamp tuples representing covered intervals.

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pub fn query_quote_ticks( &mut self, instrument_ids: Option<Vec<String>>, start: Option<u64>, end: Option<u64>, where_clause: Option<String>, ) -> PyResult<Vec<QuoteTick>>

Query quote tick data from Parquet files.

§Parameters
  • instrument_ids: Optional list of instrument IDs to filter by
  • start: Optional start timestamp (nanoseconds since Unix epoch)
  • end: Optional end timestamp (nanoseconds since Unix epoch)
  • where_clause: Optional SQL WHERE clause for additional filtering
§Returns

Returns a vector of QuoteTick objects matching the query criteria.

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pub fn query_trade_ticks( &mut self, instrument_ids: Option<Vec<String>>, start: Option<u64>, end: Option<u64>, where_clause: Option<String>, ) -> PyResult<Vec<TradeTick>>

Query trade tick data from Parquet files.

§Parameters
  • instrument_ids: Optional list of instrument IDs to filter by
  • start: Optional start timestamp (nanoseconds since Unix epoch)
  • end: Optional end timestamp (nanoseconds since Unix epoch)
  • where_clause: Optional SQL WHERE clause for additional filtering
§Returns

Returns a vector of TradeTick objects matching the query criteria.

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pub fn query_order_book_deltas( &mut self, instrument_ids: Option<Vec<String>>, start: Option<u64>, end: Option<u64>, where_clause: Option<String>, ) -> PyResult<Vec<OrderBookDelta>>

Query order book delta data from Parquet files.

§Parameters
  • instrument_ids: Optional list of instrument IDs to filter by
  • start: Optional start timestamp (nanoseconds since Unix epoch)
  • end: Optional end timestamp (nanoseconds since Unix epoch)
  • where_clause: Optional SQL WHERE clause for additional filtering
§Returns

Returns a vector of OrderBookDelta objects matching the query criteria.

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pub fn query_bars( &mut self, instrument_ids: Option<Vec<String>>, start: Option<u64>, end: Option<u64>, where_clause: Option<String>, ) -> PyResult<Vec<Bar>>

Query bar data from Parquet files.

§Parameters
  • instrument_ids: Optional list of instrument IDs to filter by
  • start: Optional start timestamp (nanoseconds since Unix epoch)
  • end: Optional end timestamp (nanoseconds since Unix epoch)
  • where_clause: Optional SQL WHERE clause for additional filtering
§Returns

Returns a vector of Bar objects matching the query criteria.

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pub fn query_order_book_depths( &mut self, instrument_ids: Option<Vec<String>>, start: Option<u64>, end: Option<u64>, where_clause: Option<String>, ) -> PyResult<Vec<OrderBookDepth10>>

Query order book depth data from Parquet files.

§Parameters
  • instrument_ids: Optional list of instrument IDs to filter by
  • start: Optional start timestamp (nanoseconds since Unix epoch)
  • end: Optional end timestamp (nanoseconds since Unix epoch)
  • where_clause: Optional SQL WHERE clause for additional filtering
§Returns

Returns a vector of OrderBookDepth10 objects matching the query criteria.

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pub fn query_mark_price_updates( &mut self, instrument_ids: Option<Vec<String>>, start: Option<u64>, end: Option<u64>, where_clause: Option<String>, ) -> PyResult<Vec<MarkPriceUpdate>>

Query mark price update data from Parquet files.

§Parameters
  • instrument_ids: Optional list of instrument IDs to filter by
  • start: Optional start timestamp (nanoseconds since Unix epoch)
  • end: Optional end timestamp (nanoseconds since Unix epoch)
  • where_clause: Optional SQL WHERE clause for additional filtering
§Returns

Returns a vector of MarkPriceUpdate objects matching the query criteria.

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pub fn query_index_price_updates( &mut self, instrument_ids: Option<Vec<String>>, start: Option<u64>, end: Option<u64>, where_clause: Option<String>, ) -> PyResult<Vec<IndexPriceUpdate>>

Query index price update data from Parquet files.

§Parameters
  • instrument_ids: Optional list of instrument IDs to filter by
  • start: Optional start timestamp (nanoseconds since Unix epoch)
  • end: Optional end timestamp (nanoseconds since Unix epoch)
  • where_clause: Optional SQL WHERE clause for additional filtering
§Returns

Returns a vector of IndexPriceUpdate objects matching the query criteria.

Trait Implementations§

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impl<'py> IntoPyObject<'py> for ParquetDataCatalogV2

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type Target = ParquetDataCatalogV2

The Python output type
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type Output = Bound<'py, <ParquetDataCatalogV2 as IntoPyObject<'py>>::Target>

The smart pointer type to use. Read more
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type Error = PyErr

The type returned in the event of a conversion error.
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fn into_pyobject( self, py: Python<'py>, ) -> Result<<Self as IntoPyObject<'_>>::Output, <Self as IntoPyObject<'_>>::Error>

Performs the conversion.
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impl PyClass for ParquetDataCatalogV2

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type Frozen = False

Whether the pyclass is frozen. Read more
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impl PyClassImpl for ParquetDataCatalogV2

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const IS_BASETYPE: bool = false

#[pyclass(subclass)]
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const IS_SUBCLASS: bool = false

#[pyclass(extends=…)]
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const IS_MAPPING: bool = false

#[pyclass(mapping)]
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const IS_SEQUENCE: bool = false

#[pyclass(sequence)]
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const IS_IMMUTABLE_TYPE: bool = false

#[pyclass(immutable_type)]
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type BaseType = PyAny

Base class
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type ThreadChecker = SendablePyClass<ParquetDataCatalogV2>

This handles following two situations: Read more
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type PyClassMutability = <<PyAny as PyClassBaseType>::PyClassMutability as PyClassMutability>::MutableChild

Immutable or mutable
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type Dict = PyClassDummySlot

Specify this class has #[pyclass(dict)] or not.
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type WeakRef = PyClassDummySlot

Specify this class has #[pyclass(weakref)] or not.
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type BaseNativeType = PyAny

The closest native ancestor. This is PyAny by default, and when you declare #[pyclass(extends=PyDict)], it’s PyDict.
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fn items_iter() -> PyClassItemsIter

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fn doc(py: Python<'_>) -> PyResult<&'static CStr>

Rendered class doc
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fn lazy_type_object() -> &'static LazyTypeObject<Self>

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fn dict_offset() -> Option<isize>

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fn weaklist_offset() -> Option<isize>

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impl PyClassNewTextSignature<ParquetDataCatalogV2> for PyClassImplCollector<ParquetDataCatalogV2>

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fn new_text_signature(self) -> Option<&'static str>

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impl<'a, 'py> PyFunctionArgument<'a, 'py, false> for &'a ParquetDataCatalogV2

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type Holder = Option<PyRef<'py, ParquetDataCatalogV2>>

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fn extract( obj: &'a Bound<'py, PyAny>, holder: &'a mut Self::Holder, ) -> PyResult<Self>

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impl<'a, 'py> PyFunctionArgument<'a, 'py, false> for &'a mut ParquetDataCatalogV2

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type Holder = Option<PyRefMut<'py, ParquetDataCatalogV2>>

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fn extract( obj: &'a Bound<'py, PyAny>, holder: &'a mut Self::Holder, ) -> PyResult<Self>

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impl PyMethods<ParquetDataCatalogV2> for PyClassImplCollector<ParquetDataCatalogV2>

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fn py_methods(self) -> &'static PyClassItems

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impl PyTypeInfo for ParquetDataCatalogV2

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const NAME: &'static str = "ParquetDataCatalogV2"

Class name.
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const MODULE: Option<&'static str>

Module name, if any.
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fn type_object_raw(py: Python<'_>) -> *mut PyTypeObject

Returns the PyTypeObject instance for this type.
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Returns the safe abstraction over the type object.
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Checks if object is an instance of this type or a subclass of this type.
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fn is_exact_type_of(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type.
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impl DerefToPyAny for ParquetDataCatalogV2

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