def get_rows_data(rows_files):
file_sizes = []
partition_bounds = []
parts_file = [
x['path'] for x in rows_files if x['path'].endswith('parts')
]
if parts_file:
parts = hadoop_ls(parts_file[0])
for i, x in enumerate(parts):
index = x['path'].split(f'{parts_file[0]}/part-')[1].split(
'-')[0]
if i < len(parts) - 1:
test_index = parts[i + 1]['path'].split(
f'{parts_file[0]}/part-')[1].split('-')[0]
if test_index == index:
continue
file_sizes.append(x['size_bytes'])
metadata_file = [
x['path'] for x in rows_files
if x['path'].endswith('metadata.json.gz')
]
if metadata_file:
with hadoop_open(metadata_file[0], 'rb') as f:
rows_meta = json.loads(f.read())
try:
partition_bounds = [(x['start']['locus']['contig'],
x['start']['locus']['position'],
x['end']['locus']['contig'],
x['end']['locus']['position'])
for x in rows_meta['jRangeBounds']]
except KeyError:
pass
return partition_bounds, file_sizes
def hail_metadata(t_path):
"""Create a metadata plot for a Hail Table or MatrixTable.
Parameters
----------
t_path : str
Path to the Hail Table or MatrixTable files.
Returns
-------
:class:`bokeh.plotting.figure.Figure` or :class:`bokeh.models.widgets.panels.Tabs` or :class:`bokeh.models.layouts.Column`
"""
def get_rows_data(rows_files):
file_sizes = []
partition_bounds = []
parts_file = [
x['path'] for x in rows_files if x['path'].endswith('parts')
]
if parts_file:
parts = hadoop_ls(parts_file[0])
for i, x in enumerate(parts):
index = x['path'].split(f'{parts_file[0]}/part-')[1].split(
'-')[0]
if i < len(parts) - 1:
test_index = parts[i + 1]['path'].split(
f'{parts_file[0]}/part-')[1].split('-')[0]
if test_index == index:
continue
file_sizes.append(x['size_bytes'])
metadata_file = [
x['path'] for x in rows_files
if x['path'].endswith('metadata.json.gz')
]
if metadata_file:
with hadoop_open(metadata_file[0], 'rb') as f:
rows_meta = json.loads(f.read())
try:
partition_bounds = [(x['start']['locus']['contig'],
x['start']['locus']['position'],
x['end']['locus']['contig'],
x['end']['locus']['position'])
for x in rows_meta['jRangeBounds']]
except KeyError:
pass
return partition_bounds, file_sizes
def scale_file_sizes(file_sizes):
min_file_size = min(file_sizes) * 1.1
total_file_size = sum(file_sizes)
all_scales = [('T', 1e12), ('G', 1e9), ('M', 1e6), ('K', 1e3),
('', 1e0)]
for overall_scale, overall_factor in all_scales:
if total_file_size > overall_factor:
total_file_size /= overall_factor
break
for scale, factor in all_scales:
if min_file_size > factor:
file_sizes = [x / factor for x in file_sizes]
break
total_file_size = f'{total_file_size:.1f} {overall_scale}B'
return total_file_size, file_sizes, scale
files = hadoop_ls(t_path)
rows_file = [x['path'] for x in files if x['path'].endswith('rows')]
entries_file = [x['path'] for x in files if x['path'].endswith('entries')]
success_file = [
x['modification_time'] for x in files if x['path'].endswith('SUCCESS')
]
metadata_file = [
x['path'] for x in files if x['path'].endswith('metadata.json.gz')
]
if not metadata_file:
raise FileNotFoundError('No metadata.json.gz file found.')
with hadoop_open(metadata_file[0], 'rb') as f:
overall_meta = json.loads(f.read())
rows_per_partition = overall_meta['components']['partition_counts'][
'counts']
if not rows_file:
raise FileNotFoundError('No rows directory found.')
rows_files = hadoop_ls(rows_file[0])
data_type = 'Table'
if entries_file:
data_type = 'MatrixTable'
rows_file = [
x['path'] for x in rows_files if x['path'].endswith('rows')
]
rows_files = hadoop_ls(rows_file[0])
row_partition_bounds, row_file_sizes = get_rows_data(rows_files)
total_file_size, row_file_sizes, row_scale = scale_file_sizes(
row_file_sizes)
panel_size = 480
subpanel_size = 120
if not row_partition_bounds:
warning('Table is not partitioned. Only plotting file sizes')
row_file_sizes_hist, row_file_sizes_edges = np.histogram(
row_file_sizes, bins=50)
p_file_size = figure(plot_width=panel_size, plot_height=panel_size)
p_file_size.quad(right=row_file_sizes_hist,
left=0,
bottom=row_file_sizes_edges[:-1],
top=row_file_sizes_edges[1:],
fill_color="#036564",
line_color="#033649")
p_file_size.yaxis.axis_label = f'File size ({row_scale}B)'
return p_file_size
all_data = {
'partition_widths':
[-1 if x[0] != x[2] else x[3] - x[1] for x in row_partition_bounds],
'partition_bounds':
[f'{x[0]}:{x[1]}-{x[2]}:{x[3]}' for x in row_partition_bounds],
'spans_chromosome': [
'Spans chromosomes' if x[0] != x[2] else 'Within chromosome'
for x in row_partition_bounds
],
'row_file_sizes':
row_file_sizes,
'row_file_sizes_human':
[f'{x:.1f} {row_scale}B' for x in row_file_sizes],
'rows_per_partition':
rows_per_partition,
'index':
list(range(len(rows_per_partition)))
}
if entries_file:
entries_rows_files = hadoop_ls(entries_file[0])
entries_rows_file = [
x['path'] for x in entries_rows_files if x['path'].endswith('rows')
]
if entries_rows_file:
entries_files = hadoop_ls(entries_rows_file[0])
entry_partition_bounds, entry_file_sizes = get_rows_data(
entries_files)
total_entry_file_size, entry_file_sizes, entry_scale = scale_file_sizes(
entry_file_sizes)
all_data['entry_file_sizes'] = entry_file_sizes
all_data['entry_file_sizes_human'] = [
f'{x:.1f} {entry_scale}B' for x in row_file_sizes
]
title = f'{data_type}: {t_path}'
msg = f"Rows: {sum(all_data['rows_per_partition']):,}<br/>Partitions: {len(all_data['rows_per_partition']):,}<br/>Size: {total_file_size}<br/>"
if success_file[0]:
msg += success_file[0]
tools = "hover,save,pan,box_zoom,reset,wheel_zoom"
source = ColumnDataSource(pd.DataFrame(all_data))
p = figure(tools=tools, plot_width=panel_size, plot_height=panel_size)
p.title.text = title
p.xaxis.axis_label = 'Number of rows'
p.yaxis.axis_label = f'File size ({row_scale}B)'
color_map = factor_cmap('spans_chromosome',
palette=Spectral8,
factors=list(set(all_data['spans_chromosome'])))
p.scatter('rows_per_partition',
'row_file_sizes',
color=color_map,
legend='spans_chromosome',
source=source)
p.legend.location = 'bottom_right'
p.select_one(HoverTool).tooltips = [
(x, f'@{x}') for x in ('rows_per_partition', 'row_file_sizes_human',
'partition_bounds', 'index')
]
p_stats = Div(text=msg)
p_rows_per_partition = figure(x_range=p.x_range,
plot_width=panel_size,
plot_height=subpanel_size)
p_file_size = figure(y_range=p.y_range,
plot_width=subpanel_size,
plot_height=panel_size)
rows_per_partition_hist, rows_per_partition_edges = np.histogram(
all_data['rows_per_partition'], bins=50)
p_rows_per_partition.quad(top=rows_per_partition_hist,
bottom=0,
left=rows_per_partition_edges[:-1],
right=rows_per_partition_edges[1:],
fill_color="#036564",
line_color="#033649")
row_file_sizes_hist, row_file_sizes_edges = np.histogram(
all_data['row_file_sizes'], bins=50)
p_file_size.quad(right=row_file_sizes_hist,
left=0,
bottom=row_file_sizes_edges[:-1],
top=row_file_sizes_edges[1:],
fill_color="#036564",
line_color="#033649")
rows_grid = gridplot([[p_rows_per_partition, p_stats], [p, p_file_size]])
if 'entry_file_sizes' in all_data:
title = f'Statistics for {data_type}: {t_path}'
msg = f"Rows: {sum(all_data['rows_per_partition']):,}<br/>Partitions: {len(all_data['rows_per_partition']):,}<br/>Size: {total_entry_file_size}<br/>"
if success_file[0]:
msg += success_file[0]
source = ColumnDataSource(pd.DataFrame(all_data))
p = figure(tools=tools, plot_width=panel_size, plot_height=panel_size)
p.title.text = title
p.xaxis.axis_label = 'Number of rows'
p.yaxis.axis_label = f'File size ({entry_scale}B)'
color_map = factor_cmap('spans_chromosome',
palette=Spectral8,
factors=list(set(
all_data['spans_chromosome'])))
p.scatter('rows_per_partition',
'entry_file_sizes',
color=color_map,
legend='spans_chromosome',
source=source)
p.legend.location = 'bottom_right'
p.select_one(HoverTool).tooltips = [
(x, f'@{x}')
for x in ('rows_per_partition', 'entry_file_sizes_human',
'partition_bounds', 'index')
]
p_stats = Div(text=msg)
p_rows_per_partition = figure(x_range=p.x_range,
plot_width=panel_size,
plot_height=subpanel_size)
p_rows_per_partition.quad(top=rows_per_partition_hist,
bottom=0,
left=rows_per_partition_edges[:-1],
right=rows_per_partition_edges[1:],
fill_color="#036564",
line_color="#033649")
p_file_size = figure(y_range=p.y_range,
plot_width=subpanel_size,
plot_height=panel_size)
row_file_sizes_hist, row_file_sizes_edges = np.histogram(
all_data['entry_file_sizes'], bins=50)
p_file_size.quad(right=row_file_sizes_hist,
left=0,
bottom=row_file_sizes_edges[:-1],
top=row_file_sizes_edges[1:],
fill_color="#036564",
line_color="#033649")
entries_grid = gridplot([[p_rows_per_partition, p_stats],
[p, p_file_size]])
return Tabs(tabs=[
Panel(child=entries_grid, title='Entries'),
Panel(child=rows_grid, title='Rows')
])
else:
return rows_grid
