Added specific to record/ folder micro benchmarks to get exact speed change after updating to V2 message format

3 files changed, 155 insertions, 0 deletions

diff --git a/benchmarks/README b/benchmarks/README
new file mode 100644
index 0000000..369e8b6
--- /dev/null
+++ b/benchmarks/README
@@ -0,0 +1,4 @@
+The `record_batch_*` benchmarks in this section are written using
+``perf`` library, created by Viktor Stinner. For more information on how to get
+reliable results of test runs please consult
+http://perf.readthedocs.io/en/latest/run_benchmark.html.
diff --git a/benchmarks/record_batch_compose.py b/benchmarks/record_batch_compose.py
new file mode 100644
index 0000000..11320ca
--- /dev/null
+++ b/benchmarks/record_batch_compose.py
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+#!/usr/bin/env python3
+from __future__ import print_function
+import perf
+from kafka.record.memory_records import MemoryRecordsBuilder
+import itertools
+import random
+import hashlib
+import os
+
+
+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)
diff --git a/benchmarks/record_batch_read.py b/benchmarks/record_batch_read.py
new file mode 100644
index 0000000..4ded5b0
--- /dev/null
+++ b/benchmarks/record_batch_read.py
@@ -0,0 +1,78 @@
+#!/usr/bin/env python
+from __future__ import print_function
+import perf
+from kafka.record.memory_records import MemoryRecords, MemoryRecordsBuilder
+import itertools
+import random
+import hashlib
+import os
+
+
+DEFAULT_BATCH_SIZE = 1600 * 1024
+KEY_SIZE = 6
+VALUE_SIZE = 60
+TIMESTAMP_RANGE = [1505824130000, 1505824140000]
+
+BATCH_SAMPLES = 5
+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(magic):
+    samples = []
+    for _ in range(BATCH_SAMPLES):
+        batch = MemoryRecordsBuilder(
+            magic, batch_size=DEFAULT_BATCH_SIZE, compression_type=0)
+        for _ in range(MESSAGES_PER_BATCH):
+            size = batch.append(
+                random.randint(*TIMESTAMP_RANGE),
+                random_bytes(KEY_SIZE),
+                random_bytes(VALUE_SIZE))
+            assert size
+        batch.close()
+        samples.append(bytes(batch.buffer()))
+
+    return iter(itertools.cycle(samples))
+
+
+def finalize(results):
+    # Just some strange code to make sure PyPy does execute the code above
+    # properly
+    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(magic)
+    results = []
+
+    # Main benchmark code.
+    batch_data = next(precomputed_samples)
+    t0 = perf.perf_counter()
+    for _ in range(loops):
+        records = MemoryRecords(batch_data)
+        while records.has_next():
+            batch = records.next_batch()
+            batch.validate_crc()
+            for record in batch:
+                results.append(record.value)
+
+    res = perf.perf_counter() - t0
+    finalize(results)
+
+    return res
+
+
+runner = perf.Runner()
+runner.bench_time_func('batch_read_v0', func, 0)
+runner.bench_time_func('batch_read_v1', func, 1)