smallpond.execution.task.HashPartitionTask#

class smallpond.execution.task.HashPartitionTask(ctx: RuntimeContext, input_deps: List[Task], partition_infos: List[PartitionInfo], npartitions: int, dimension: str, hash_columns: List[str], data_partition_column: str, random_shuffle: bool = False, shuffle_only: bool = False, drop_partition_column=False, use_parquet_writer=False, hive_partitioning=False, parquet_row_group_size: int = 122880, parquet_dictionary_encoding=False, parquet_compression='ZSTD', parquet_compression_level=3, output_name: str | None = None, output_path: str | None = None, cpu_limit: int | None = None, memory_limit: int | None = None)#

__init__(ctx: RuntimeContext, input_deps: List[Task], partition_infos: List[PartitionInfo], npartitions: int, dimension: str, hash_columns: List[str], data_partition_column: str, random_shuffle: bool = False, shuffle_only: bool = False, drop_partition_column=False, use_parquet_writer=False, hive_partitioning=False, parquet_row_group_size: int = 122880, parquet_dictionary_encoding=False, parquet_compression='ZSTD', parquet_compression_level=3, output_name: str | None = None, output_path: str | None = None, cpu_limit: int | None = None, memory_limit: int | None = None) → None#

Methods

`__init__`(ctx, input_deps, partition_infos, ...)
`add_elapsed_time`([metric_name])	Start or stop the timer.
`adjust_row_group_size`(nbytes, num_rows[, ...])
`clean_complex_attrs`()
`clean_output`([force])
`cleanup`()	Called after run() even if there is an exception.
`compute_avg_row_size`(nbytes, num_rows)
`create`(engine_type, args, *kwargs)
`dump`()
`exec`([cq])
`finalize`()	Called after run() to finalize the processing.
`get_partition_info`(dimension)
`initialize`()	Called before run() to prepare for running the task.
`inject_fault`()
`merge_metrics`(metrics)
`oom`([nonzero_exitcode_as_oom])
`parquet_kv_metadata_bytes`([extra_partitions])
`parquet_kv_metadata_str`([extra_partitions])
`partition`(input_dataset)
`random_float`()
`random_uint32`()
`run`()
`run_on_ray`()	Run the task on Ray.
`set_memory_limit`(soft_limit, hard_limit)

Attributes

`hash_columns`
`data_partition_column`
`random_shuffle`
`shuffle_only`
`drop_partition_column`
`use_parquet_writer`
`hive_partitioning`
`parquet_row_group_size`
`parquet_row_group_bytes`
`parquet_dictionary_encoding`
`parquet_compression`
`parquet_compression_level`
`partitioned_datasets`
`allow_speculative_exec`	Whether the task is allowed to be executed by speculative execution.
`any_input_empty`
`cpu_limit`
`ctx`
`dataset`
`default_output_name`
`dimension`
`elapsed_time`
`exception`
`exec_cq`
`exec_id`
`exec_on_scheduler`
`fail_count`
`final_output_abspath`
`finish_time`
`gpu_limit`
`id`
`input_datasets`
`input_deps`
`io_workers`
`key`
`local_gpu`	Return the first GPU granted to this task.
`local_gpu_ranks`	Return all GPU ranks granted to this task.
`local_rank`	Return the first GPU rank granted to this task.
`location`
`max_batch_size`
`memory_limit`
`node_id`
`npartitions`
`num_workers`
`numa_node`
`numpy_random_gen`
`output`
`output_deps`
`output_dirname`
`output_filename`
`output_name`
`output_root`
`partition_dims`
`partition_infos`
`partition_infos_as_dict`
`perf_metrics`
`perf_profile`
`python_random_gen`
`random_seed_bytes`
`ray_dataset_path`	The path of a pickle file that contains the output dataset of the task.
`ray_marker_path`	The path of an empty file that is used to determine if the task has been started.
`retry_count`
`runtime_id`
`runtime_output_abspath`	Output data will be produced in this directory at runtime.
`runtime_state`
`sched_epoch`
`self_contained_output`	Whether the output of this node is not dependent on any input nodes.
`skip_when_any_input_empty`
`staging_root`	If the task has a special output directory, its runtime output directory will be under it.
`start_time`
`status`
`temp_abspath`
`temp_output`	Whether the output of this node is stored in a temporary directory.
`uniform_failure_prob`
`write_buffer_size`