1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
|
=========================
Object Storage monitoring
=========================
.. note::
This section was excerpted from a `blog post by Darrell
Bishop <https://swiftstack.com/blog/2012/04/11/swift-monitoring-with-statsd>`_ and
has since been edited.
An OpenStack Object Storage cluster is a collection of many daemons that
work together across many nodes. With so many different components, you
must be able to tell what is going on inside the cluster. Tracking
server-level meters like CPU utilization, load, memory consumption, disk
usage and utilization, and so on is necessary, but not sufficient.
Swift Recon
~~~~~~~~~~~
The Swift Recon middleware (see :ref:`cluster_telemetry_and_monitoring`)
provides general machine statistics, such as load average, socket
statistics, ``/proc/meminfo`` contents, as well as Swift-specific meters:
- The ``MD5`` sum of each ring file.
- The most recent object replication time.
- Count of each type of quarantined file: Account, container, or
object.
- Count of "async_pendings" (deferred container updates) on disk.
Swift Recon is middleware that is installed in the object servers
pipeline and takes one required option: A local cache directory. To
track ``async_pendings``, you must set up an additional cron job for
each object server. You access data by either sending HTTP requests
directly to the object server or using the ``swift-recon`` command-line
client.
There are Object Storage cluster statistics but the typical
server meters overlap with existing server monitoring systems. To get
the Swift-specific meters into a monitoring system, they must be polled.
Swift Recon acts as a middleware meters collector. The
process that feeds meters to your statistics system, such as
``collectd`` and ``gmond``, should already run on the storage node.
You can choose to either talk to Swift Recon or collect the meters
directly.
Swift-Informant
~~~~~~~~~~~~~~~
Swift-Informant middleware (see
`swift-informant <https://github.com/pandemicsyn/swift-informant>`_) has
real-time visibility into Object Storage client requests. It sits in the
pipeline for the proxy server, and after each request to the proxy server it
sends three meters to a ``StatsD`` server:
- A counter increment for a meter like ``obj.GET.200`` or
``cont.PUT.404``.
- Timing data for a meter like ``acct.GET.200`` or ``obj.GET.200``.
[The README says the meters look like ``duration.acct.GET.200``, but
I do not see the ``duration`` in the code. I am not sure what the
Etsy server does but our StatsD server turns timing meters into five
derivative meters with new segments appended, so it probably works as
coded. The first meter turns into ``acct.GET.200.lower``,
``acct.GET.200.upper``, ``acct.GET.200.mean``,
``acct.GET.200.upper_90``, and ``acct.GET.200.count``].
- A counter increase by the bytes transferred for a meter like
``tfer.obj.PUT.201``.
This is used for receiving information on the quality of service clients
experience with the timing meters, as well as sensing the volume of the
various modifications of a request server type, command, and response
code. Swift-Informant requires no change to core Object
Storage code because it is implemented as middleware. However, it gives
no insight into the workings of the cluster past the proxy server.
If the responsiveness of one storage node degrades, you can only see
that some of the requests are bad, either as high latency or error
status codes.
Statsdlog
~~~~~~~~~
The `Statsdlog <https://github.com/pandemicsyn/statsdlog>`_
project increments StatsD counters based on logged events. Like
Swift-Informant, it is also non-intrusive, however statsdlog can track
events from all Object Storage daemons, not just proxy-server. The
daemon listens to a UDP stream of syslog messages, and StatsD counters
are incremented when a log line matches a regular expression. Meter
names are mapped to regex match patterns in a JSON file, allowing
flexible configuration of what meters are extracted from the log stream.
Currently, only the first matching regex triggers a StatsD counter
increment, and the counter is always incremented by one. There is no way
to increment a counter by more than one or send timing data to StatsD
based on the log line content. The tool could be extended to handle more
meters for each line and data extraction, including timing data. But a
coupling would still exist between the log textual format and the log
parsing regexes, which would themselves be more complex to support
multiple matches for each line and data extraction. Also, log processing
introduces a delay between the triggering event and sending the data to
StatsD. It would be preferable to increment error counters where they
occur and send timing data as soon as it is known to avoid coupling
between a log string and a parsing regex and prevent a time delay
between events and sending data to StatsD.
The next section describes another method for gathering Object Storage
operational meters.
Swift StatsD logging
~~~~~~~~~~~~~~~~~~~~
StatsD (see `Measure Anything, Measure Everything
<https://codeascraft.com/2011/02/15/measure-anything-measure-everything/>`_)
was designed for application code to be deeply instrumented. Meters are
sent in real-time by the code that just noticed or did something. The
overhead of sending a meter is extremely low: a ``sendto`` of one UDP
packet. If that overhead is still too high, the StatsD client library
can send only a random portion of samples and StatsD approximates the
actual number when flushing meters upstream.
To avoid the problems inherent with middleware-based monitoring and
after-the-fact log processing, the sending of StatsD meters is
integrated into Object Storage itself. Details of the meters tracked
are in the :doc:`/admin_guide`.
The sending of meters is integrated with the logging framework. To
enable, configure ``log_statsd_host`` in the relevant config file. You
can also specify the port and a default sample rate. The specified
default sample rate is used unless a specific call to a statsd logging
method (see the list below) overrides it. Currently, no logging calls
override the sample rate, but it is conceivable that some meters may
require accuracy (``sample_rate=1``) while others may not.
.. code-block:: ini
[DEFAULT]
# ...
log_statsd_host = 127.0.0.1
log_statsd_port = 8125
log_statsd_default_sample_rate = 1
Then the LogAdapter object returned by ``get_logger()``, usually stored
in ``self.logger``, has these new methods:
- ``set_statsd_prefix(self, prefix)`` Sets the client library stat
prefix value which gets prefixed to every meter. The default prefix
is the ``name`` of the logger such as ``object-server``,
``container-auditor``, and so on. This is currently used to turn
``proxy-server`` into one of ``proxy-server.Account``,
``proxy-server.Container``, or ``proxy-server.Object`` as soon as the
Controller object is determined and instantiated for the request.
- ``update_stats(self, metric, amount, sample_rate=1)`` Increments
the supplied meter by the given amount. This is used when you need
to add or subtract more that one from a counter, like incrementing
``suffix.hashes`` by the number of computed hashes in the object
replicator.
- ``increment(self, metric, sample_rate=1)`` Increments the given counter
meter by one.
- ``decrement(self, metric, sample_rate=1)`` Lowers the given counter
meter by one.
- ``timing(self, metric, timing_ms, sample_rate=1)`` Record that the
given meter took the supplied number of milliseconds.
- ``timing_since(self, metric, orig_time, sample_rate=1)``
Convenience method to record a timing meter whose value is "now"
minus an existing timestamp.
.. note::
These logging methods may safely be called anywhere you have a
logger object. If StatsD logging has not been configured, the methods
are no-ops. This avoids messy conditional logic each place a meter is
recorded. These example usages show the new logging methods:
.. code-block:: python
# swift/obj/replicator.py
def update(self, job):
# ...
begin = time.time()
try:
hashed, local_hash = tpool.execute(tpooled_get_hashes, job['path'],
do_listdir=(self.replication_count % 10) == 0,
reclaim_age=self.reclaim_age)
# See tpooled_get_hashes "Hack".
if isinstance(hashed, BaseException):
raise hashed
self.suffix_hash += hashed
self.logger.update_stats('suffix.hashes', hashed)
# ...
finally:
self.partition_times.append(time.time() - begin)
self.logger.timing_since('partition.update.timing', begin)
.. code-block:: python
# swift/container/updater.py
def process_container(self, dbfile):
# ...
start_time = time.time()
# ...
for event in events:
if 200 <= event.wait() < 300:
successes += 1
else:
failures += 1
if successes > failures:
self.logger.increment('successes')
# ...
else:
self.logger.increment('failures')
# ...
# Only track timing data for attempted updates:
self.logger.timing_since('timing', start_time)
else:
self.logger.increment('no_changes')
self.no_changes += 1
|
