def test_simple(self):
frame = zstd.compress(b"foobar")
fp = zstd.get_frame_parameters(frame)
self.assertEqual(fp.content_size, 6)
self.assertFalse(fp.has_checksum)
zstd.compress(b"foobar" * 16384, level=7)
def transmit(result, sock):
pickler = pickle.Pickler(sock);
cols = list(result.keys());
pickler.dump(cols);
for col in cols:
if(result[col].dtype == object):
colz = zstd.compress(pickle.dumps(result[col]))
else:
colz = zstd.compress(result[col]);
pickler.dump(result[col].dtype);
pickler.dump(colz);
def compress_data(self):
if self.settings['use_lzma']:
print('[*] Compressing texture with lzma')
filters = [
{
"id": lzma.FILTER_LZMA1,
"dict_size": 256 * 1024,
"lc": 3,
"lp": 0,
"pb": 2,
"mode": lzma.MODE_NORMAL
},
]
compressed = lzma.compress(self.buffer, format=lzma.FORMAT_ALONE, filters=filters)
compressed = compressed[0:5] + len(self.buffer).to_bytes(4, 'little') + compressed[13:]
elif self.settings['use_lzham']:
print('[*] Compressing texture with lzham')
dict_size = 18
compressed = lzham.compress(self.buffer, {'dict_size_log2': dict_size})
compressed = 'SCLZ'.encode('utf-8') + dict_size.to_bytes(1, 'big') + len(self.buffer).to_bytes(4, 'little') + compressed
else:
print('[*] Compressing texture with zstandard')
compressed = zstandard.compress(self.buffer, level=zstandard.MAX_COMPRESSION_LEVEL)
fileMD5 = hashlib.md5(self.buffer).digest()
# Flush the previous buffer
self.buffer = b''
if self.settings['header']:
self.write('SC'.encode('utf-8'))
if self.settings['use_zstd']:
self.write_uint32(3, 'big')
else:
self.write_uint32(1, 'big')
self.write_uint32(len(fileMD5), 'big')
self.write(fileMD5)
print('[*] Header wrote !')
self.write(compressed)
print('[*] Compression done !')
def __serialize(self) -> bytes:
batch = tsbatch_pb2.Batch()
for timeseries in self.__timeseries.values():
protobuf_ts = batch.timeseries.add()
timeseries.serialize_to(protobuf_ts=protobuf_ts)
frame = tsbatch_pb2.Frame()
frame.contentType = tsbatch_pb2.Frame.ContentType.ZSTD_COMPRESSED_BATCH
# level 14 has been empirically determined as the threshold for dimnishing returns
content = zstandard.compress(batch.SerializeToString(), level=14)
h = hashlib.sha3_512()
h.update(content)
frame.content = content
frame.messageId = h.hexdigest()
self.__message = frame.SerializeToString()
def mutation_create(item):
path, source, coverage, mutation_predicate = item
if not coverage:
msg = "Ignoring file {} because there is no associated coverage."
log.trace(msg, path)
return []
log.trace("Mutating file: {}...", path)
mutations = [m for m in Mutation.ALL if mutation_predicate(m)]
deltas = deltas_compute(source, path, coverage, mutations)
# return the compressed deltas to save some time in the
# mainthread.
out = [(path, zstd.compress(x.encode("utf8"))) for x in deltas]
log.trace("There is {} mutations for the file `{}`", len(out), path)
return out
async def index(tx, store, docuid, counter):
# translate keys that are string tokens, into uuid4 bytes with
# store.tokens
tokens = dict()
for string, count in counter.items():
query = nstore.select(tx, store.tokens, string, nstore.var('uid'))
try:
uid = await query.__anext__()
except StopAsyncIteration:
uid = uuid4()
nstore.add(tx, store.tokens, string, uid)
else:
uid = uid['uid']
tokens[uid] = count
# store tokens to use later during search for filtering
found.set(tx, found.pack((store.prefix_counters, docuid)),
zstd.compress(found.pack(tuple(tokens.items()))))
# store tokens keys for candidate selection
for token in tokens:
found.set(tx, found.pack((store.prefix_index, token, docuid)), b'')
def serialize_message(message: BaseModel, compress: bool = False) -> bytes:
data = message.json().encode("utf-8")
if compress:
data = b"Z" + zstandard.compress(data)
return data
def test_simple(self):
source = b"foobar" * 8192
frame = zstd.compress(source)
self.assertEqual(zstd.decompress(frame), source)
def commit(self):
committed_blob_name = ""
if len(self.buffer) > 0:
lock = threading.Lock()
try:
lock.acquire(blocking=True, timeout=10)
if self.format == "parquet":
try:
import pyarrow.json
import pyarrow.parquet as pq # type:ignore
except ImportError as err: # pragma: no cover
raise MissingDependencyError(
"`pyarrow` is missing, please install or includein requirements.txt"
)
import io
tempfile = io.BytesIO()
temp_list = [
orjson.loads(record)
for record in self.buffer.splitlines()
]
pytable = pyarrow.Table.from_pylist(temp_list)
pyarrow.parquet.write_table(pytable,
where=tempfile,
compression="zstd")
tempfile.seek(0)
self.buffer = tempfile.read()
if self.format == "zstd":
# zstandard is an non-optional installed dependency
self.buffer = zstandard.compress(self.buffer)
committed_blob_name = self.inner_writer.commit(
byte_data=bytes(self.buffer), override_blob_name=None)
for column in self.indexes:
index = self.index_builders[column].build()
bucket, path, stem, suffix = get_parts(committed_blob_name)
index_name = f"{bucket}/{path}{stem}.{safe_field_name(column)}.idx"
committed_index_name = self.inner_writer.commit(
byte_data=index.bytes(), override_blob_name=index_name)
if "BACKOUT" in committed_blob_name:
get_logger().warning(
f"{self.records_in_buffer:n} failed records written to BACKOUT partition `{committed_blob_name}`"
)
get_logger().debug({
"committed_blob": committed_blob_name,
"records": self.records_in_buffer,
"bytes": len(self.buffer),
})
finally:
lock.release()
self.buffer = bytearray()
return committed_blob_name
def encode_base64zstd(self, layer):
''' base64 layers are one huge row, thanks to CSV assuming row data.
'''
format = '<' + 'I' * len(layer[0])
data = zstandard.compress(struct.pack(format, *(layer[0])))
return base64.b64encode(data).decode('utf-8')