Merge pull request #83 from jeromekelleher/finalise-per-array

Finalise each array as it finishes

Author: jeromekelleher

bio2zarr/vcf.py

@@ -1286,7 +1286,7 @@ def summary_table(self):
 
 @dataclasses.dataclass
 class EncodingWork:
-    func: callable
+    func: callable = dataclasses.field(repr=False)
     start: int
     stop: int
     columns: list[str]
@@ -1319,12 +1319,12 @@ def init_array(self, variable):
     def get_array(self, name):
         return self.root["wip_" + name]
 
-    def finalise_array(self, variable):
-        source = self.path / ("wip_" + variable.name)
-        dest = self.path / variable.name
+    def finalise_array(self, variable_name):
+        source = self.path / ("wip_" + variable_name)
+        dest = self.path / variable_name
         # Atomic swap
         os.rename(source, dest)
-        logger.debug(f"Finalised {variable.name}")
+        logger.info(f"Finalised {variable_name}")
 
     def encode_array_slice(self, column, start, stop):
         source_col = self.pcvcf.columns[column.vcf_field]
@@ -1471,8 +1471,8 @@ def init(self):
             self.init_array(column)
 
     def finalise(self):
-        for column in self.schema.columns.values():
-            self.finalise_array(column)
+        # for column in self.schema.columns.values():
+        #     self.finalise_array(column)
         zarr.consolidate_metadata(self.path)
 
     def encode(
@@ -1536,21 +1536,25 @@ def encode(
             work.append(
                 EncodingWork(self.encode_alleles_slice, start, stop, ["variant_allele"])
             )
-            work.append(EncodingWork(self.encode_id_slice, start, stop, ["variant_id"]))
+            work.append(
+                EncodingWork(
+                    self.encode_id_slice, start, stop, ["variant_id", "variant_id_mask"]
+                )
+            )
             work.append(
                 EncodingWork(
                     functools.partial(self.encode_filters_slice, filter_id_map),
                     start,
                     stop,
-                    ["variant_filters"],
+                    ["variant_filter"],
                 )
             )
             work.append(
                 EncodingWork(
                     functools.partial(self.encode_contig_slice, contig_id_map),
                     start,
                     stop,
-                    ["variant_contig_id"],
+                    ["variant_contig"],
                 )
             )
             if "call_genotype" in self.schema.columns:
@@ -1567,6 +1571,7 @@ def encode(
                         self.encode_genotypes_slice, start, stop, variables, gt_memory
                     )
                 )
+
         # Fail early if we can't fit a particular column into memory
         for wp in work:
             if wp.memory >= max_memory:
@@ -1581,31 +1586,47 @@ def encode(
             units="B",
             show=show_progress,
         )
-        # TODO add a map of slices completed to column here, so that we can
-        # finalise the arrays as they get completed. We'll have to service
-        # the futures more, though, not just when we exceed the memory budget
 
         used_memory = 0
+        max_queued = 4 * max(1, worker_processes)
+        encoded_slices = collections.Counter()
+
         with core.ParallelWorkManager(worker_processes, progress_config) as pwm:
             future = pwm.submit(self.encode_samples)
-            future_to_memory_use = {future: 0}
-            for wp in work:
-                while used_memory + wp.memory >= max_memory:
-                    logger.info(
-                        f"Memory budget {display_size(max_memory)} exceeded: "
-                        f"used={display_size(used_memory)} needed={display_size(wp.memory)}"
-                    )
-                    futures = pwm.wait_for_completed()
-                    released_mem = sum(
-                        future_to_memory_use.pop(future) for future in futures
-                    )
-                    logger.info(
-                        f"{len(futures)} completed, released {display_size(released_mem)}"
+            future_to_work = {future: EncodingWork(None, 0, 0, [])}
+
+            def service_completed_futures():
+                nonlocal used_memory
+
+                completed = pwm.wait_for_completed()
+                for future in completed:
+                    wp_done = future_to_work.pop(future)
+                    used_memory -= wp_done.memory
+                    logger.debug(
+                        f"Complete {wp_done}: used mem={display_size(used_memory)}"
                     )
-                    used_memory -= released_mem
+                    for column in wp_done.columns:
+                        encoded_slices[column] += 1
+                        if encoded_slices[column] == len(slices):
+                            # Do this syncronously for simplicity. Should be
+                            # fine as the workers will probably be busy with
+                            # large encode tasks most of the time.
+                            self.finalise_array(column)
+
+            for wp in work:
+                if (
+                    used_memory + wp.memory > max_memory
+                    or len(future_to_work) > max_queued
+                ):
+                    service_completed_futures()
                 future = pwm.submit(wp.func, wp.start, wp.stop)
                 used_memory += wp.memory
-                future_to_memory_use[future] = wp.memory
+                logger.debug(f"Submit {wp}: used mem={display_size(used_memory)}")
+                future_to_work[future] = wp
+
+            logger.debug("All work submitted")
+            while len(future_to_work) > 0:
+                service_completed_futures()
 
 
 def mkschema(if_path, out):