Merge pull request #1252 from dpkp/legacy_records_refactor

Refactor MessageSet and Message into LegacyRecordBatch 

1 files changed, 75 insertions, 0 deletions

diff --git a/benchmarks/record_batch_compose.py b/benchmarks/record_batch_compose.py
new file mode 100644
index 0000000..86012df
--- /dev/null
+++ b/benchmarks/record_batch_compose.py
@@ -0,0 +1,75 @@
+#!/usr/bin/env python3
+from __future__ import print_function
+import hashlib
+import itertools
+import os
+import random
+
+import perf
+
+from kafka.record.memory_records import MemoryRecordsBuilder
+
+
+DEFAULT_BATCH_SIZE = 1600 * 1024
+KEY_SIZE = 6
+VALUE_SIZE = 60
+TIMESTAMP_RANGE = [1505824130000, 1505824140000]
+
+# With values above v1 record is 100 bytes, so 10 000 bytes for 100 messages
+MESSAGES_PER_BATCH = 100
+
+
+def random_bytes(length):
+    buffer = bytearray(length)
+    for i in range(length):
+        buffer[i] = random.randint(0, 255)
+    return bytes(buffer)
+
+
+def prepare():
+    return iter(itertools.cycle([
+        (random_bytes(KEY_SIZE),
+         random_bytes(VALUE_SIZE),
+         random.randint(*TIMESTAMP_RANGE)
+         )
+        for _ in range(int(MESSAGES_PER_BATCH * 1.94))
+    ]))
+
+
+def finalize(results):
+    # Just some strange code to make sure PyPy does execute the main code
+    # properly, without optimizing it away
+    hash_val = hashlib.md5()
+    for buf in results:
+        hash_val.update(buf)
+    print(hash_val, file=open(os.devnull, "w"))
+
+
+def func(loops, magic):
+    # Jit can optimize out the whole function if the result is the same each
+    # time, so we need some randomized input data )
+    precomputed_samples = prepare()
+    results = []
+
+    # Main benchmark code.
+    t0 = perf.perf_counter()
+    for _ in range(loops):
+        batch = MemoryRecordsBuilder(
+            magic, batch_size=DEFAULT_BATCH_SIZE, compression_type=0)
+        for _ in range(MESSAGES_PER_BATCH):
+            key, value, timestamp = next(precomputed_samples)
+            size = batch.append(timestamp=timestamp, key=key, value=value)
+            assert size
+        batch.close()
+        results.append(batch.buffer())
+
+    res = perf.perf_counter() - t0
+
+    finalize(results)
+
+    return res
+
+
+runner = perf.Runner()
+runner.bench_time_func('batch_append_v0', func, 0)
+runner.bench_time_func('batch_append_v1', func, 1)