lorneli · March 18, 2017 16:12 · Mar 18, 2017 · Mar 18, 2017 · Mar 18, 2017
diff --git a/benchmark-redis-store-large-value b/benchmark-redis-store-large-value
@@ -47,8 +47,7 @@ length: 512M, spend: 5.3249s, memory 74.0%, swap 8.8%
     This test executes on a 2GB memory machine. After preparing buffers in script, memory usage has grown up to 59%.
     When setting a 512M value, machine runs out of memory and instead uses swap partition. The corresponding elapsed
     time, 5s, is nearly unacceptable for server.
-
-
+
     Before 512M, elapsed time increases linearly with the size of value. A 64M value will block others about 100ms on
     this machine.
 
diff --git a/benchmark-redis-store-large-value b/benchmark-redis-store-large-value
@@ -1,4 +1,4 @@
-Since redis uses single thread to process client requests one by one, storing large value in SET request blocks others.
+Since redis uses single thread to process client requests one by one, storing large value for a key blocks others.
 I tested how long this takes in different size value.
 
 1.Test environment
@@ -9,7 +9,7 @@ I tested how long this takes in different size value.
 2.Procedure
   Disable persistence.
   Prepare buffers to be sent
-  Send request and measure elapsed time and memory usage.
+  Send request. Measure elapsed time and memory usage.
 
 3.Script
 

diff --git a/benchmark-redis-store-large-value b/benchmark-redis-store-large-value
@@ -0,0 +1,54 @@
+Since redis uses single thread to process client requests one by one, storing large value in SET request blocks others.
+I tested how long this takes in different size value.
+
+1.Test environment
+  4core 2gb
+  ubuntu server 14.04
+  redis-version: 3.2.8
+
+2.Procedure
+  Disable persistence.
+  Prepare buffers to be sent
+  Send request and measure elapsed time and memory usage.
+
+3.Script
+
+def benchmark_large_value():
+    # Prepare values
+    lengths = [1, 4, 32, 64, 128, 256, 512]  # M
+    values = []
+    with open("/dev/urandom", "rb") as f:
+        for length in lengths:
+            value = f.read(length * 1024 * 1024)
+            values.append(value)
+
+    r = redis.StrictRedis()
+    # Send request and measure elapsed time
+    for value, length in zip(values, lengths):
+        key = "key-%d" % length
+        start = time.time()
+        r.set(key, value)
+        end = time.time()
+        print 'length: %3dM, spend: %.4fs, memory %.1f%%, swap %.1f%%' % \
+              (length, end-start, psutil.virtual_memory().percent, psutil.swap_memory().percent)
+
+4.Result
+
+length:   1M, spend: 0.0084s, memory 59.0%, swap 0.0%
+length:   4M, spend: 0.0117s, memory 59.6%, swap 0.0%
+length:  32M, spend: 0.0626s, memory 62.6%, swap 0.0%
+length:  64M, spend: 0.1335s, memory 67.6%, swap 0.0%
+length: 128M, spend: 0.2659s, memory 77.3%, swap 0.0%
+length: 256M, spend: 0.5842s, memory 96.6%, swap 0.0%
+length: 512M, spend: 5.3249s, memory 74.0%, swap 8.8%
+
+5.Conclusion
+
+    This test executes on a 2GB memory machine. After preparing buffers in script, memory usage has grown up to 59%.
+    When setting a 512M value, machine runs out of memory and instead uses swap partition. The corresponding elapsed
+    time, 5s, is nearly unacceptable for server.
+
+
+    Before 512M, elapsed time increases linearly with the size of value. A 64M value will block others about 100ms on
+    this machine.
+