Quick & Dirty Prometheus on OS-X

Install prometheus

  • Download the compiled prometheus binaries from prometheus.io
  • Unzip the binary and cd into the directory.
  • Run the prometheus binary, from the command line, it will listen on port 9090
$ cd /Users/gary.little/Downloads/prometheus-2.16.0-rc.0.darwin-amd64
$ ./prometheus
  • From a local browser, point to localhost:9090
prometheus web-ui

Add a collector/scraper to monitor the OS

Prometheus itself does not do much apart from monitor itself, to do anything useful we have to add a scraper/exporter module. The easiest thing to do is add the scraper to monitor OS-X itself. As in Linux the OS exporter is simply called “node exporter”.

Start by downloading the pre-compiled darwin node exporter from prometheus.io

  • Unzip the tar.gz
  • cd into the directory
  • run the node exporter
$ cd /Users/gary.little/Downloads/node_exporter-0.18.1.darwin-amd64
$ ./node_exporter
 INFO[0000] Starting node_exporter (version=0.18.1, branch=HEAD, revision=3db77732e925c08f675d7404a8c46466b2ece83e)  source="node_exporter.go:156"
 INFO[0000] Build context (go=go1.11.10, user=root@4a30727bb68c, date=20190604-16:47:36)  source="node_exporter.go:157"
 INFO[0000] Enabled collectors:                           source="node_exporter.go:97"
 INFO[0000]  - boottime                                   source="node_exporter.go:104"
 INFO[0000]  - cpu                                        source="node_exporter.go:104"
 INFO[0000]  - diskstats                                  source="node_exporter.go:104"
 INFO[0000]  - filesystem                                 source="node_exporter.go:104"
 INFO[0000]  - loadavg                                    source="node_exporter.go:104"
 INFO[0000]  - meminfo                                    source="node_exporter.go:104"
 INFO[0000]  - netdev                                     source="node_exporter.go:104"
 INFO[0000]  - textfile                                   source="node_exporter.go:104"
 INFO[0000]  - time                                       source="node_exporter.go:104"
 INFO[0000] Listening on :9100                            source="node_exporter.go:170""

We now have both a prometheus server and a node exporter, but they are totally independent processes. To teach the prometheus server about the node exporter, we need to edit the promethus.yml file to tell prometheus how to reach the node exporter.

Since the node exporter runs on a well-known port, we simply tell prometheus which port to talk to, then restart prometheus to pick up the new information.

Here is what my promethus.yml file looks like after adding in the node exporter. The change is that there is a “job_name” added called “node” and it lives on “localhost:9100” which is the default port number for the node exporter.

# my global config
global:
  scrape_interval:     15s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
  evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute.
  # scrape_timeout is set to the global default (10s).

# Alertmanager configuration
alerting:
  alertmanagers:
  - static_configs:
    - targets:
      # - alertmanager:9093

# Load rules once and periodically evaluate them according to the global 'evaluation_interval'.
rule_files:
  # - "first_rules.yml"
  # - "second_rules.yml"

# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
scrape_configs:
  # The job name is added as a label `job=` to any timeseries scraped from this config.
  - job_name: 'prometheus'
    static_configs:
    - targets: ['localhost:9090']

  - job_name: 'node'
    static_configs:
    - targets: ['localhost:9100']

Now restart prometheus to pick up the new config

$ ./prometheus 
level=info ts=2020-02-07T17:26:49.042Z caller=main.go:295 msg="no time or size retention was set so using the default time retention" duration=15d
level=info ts=2020-02-07T17:26:49.043Z caller=main.go:331 msg="Starting Prometheus" version="(version=2.16.0-rc.0, branch=HEAD, revision=22a04239c937be61df95fdb60f0661684693cf3b)"
level=info ts=2020-02-07T17:26:49.043Z caller=main.go:332 build_context="(go=go1.13.7, user=root@c7d619905021, date=20200131-22:56:34)"
level=info ts=2020-02-07T17:26:49.043Z caller=main.go:333 host_details=(darwin)
level=info ts=2020-02-07T17:26:49.043Z caller=main.go:334 fd_limits="(soft=256, hard=unlimited)"
level=info ts=2020-02-07T17:26:49.043Z caller=main.go:335 vm_limits="(soft=unlimited, hard=unlimited)"
level=info ts=2020-02-07T17:26:49.051Z caller=main.go:661 msg="Starting TSDB ..."
level=info ts=2020-02-07T17:26:49.051Z caller=web.go:508 component=web msg="Start listening for connections" address=0.0.0.0:9090
level=info ts=2020-02-07T17:26:49.055Z caller=repair.go:59 component=tsdb msg="found healthy block" mint=1580947174039 maxt=1580947200000 ulid=01E0C9989RFMB89NGY09SVPSC0
level=info ts=2020-02-07T17:26:49.056Z caller=repair.go:59 component=tsdb msg="found healthy block" mint=1580947200000 maxt=1580954400000 ulid=01E0C99ACPYCVKDCS1BHA0N07C
level=info ts=2020-02-07T17:26:49.057Z caller=repair.go:59 component=tsdb msg="found healthy block" mint=1580954400000 maxt=1580961600000 ulid=01E0D87R60GZV9HPPFX7CSDWZM
level=info ts=2020-02-07T17:26:49.058Z caller=repair.go:59 component=tsdb msg="found healthy block" mint=1580990400000 maxt=1580997600000 ulid=01E0DG80PJKN99HQF01V1EKAH1
level=info ts=2020-02-07T17:26:49.075Z caller=head.go:577 component=tsdb msg="replaying WAL, this may take awhile"
level=info ts=2020-02-07T17:26:49.081Z caller=head.go:601 component=tsdb msg="WAL checkpoint loaded"
level=info ts=2020-02-07T17:26:49.082Z caller=head.go:625 component=tsdb msg="WAL segment loaded" segment=10 maxSegment=14
level=info ts=2020-02-07T17:26:49.084Z caller=head.go:625 component=tsdb msg="WAL segment loaded" segment=11 maxSegment=14
level=info ts=2020-02-07T17:26:49.087Z caller=head.go:625 component=tsdb msg="WAL segment loaded" segment=12 maxSegment=14
level=info ts=2020-02-07T17:26:49.115Z caller=head.go:625 component=tsdb msg="WAL segment loaded" segment=13 maxSegment=14
level=info ts=2020-02-07T17:26:49.116Z caller=head.go:625 component=tsdb msg="WAL segment loaded" segment=14 maxSegment=14
level=info ts=2020-02-07T17:26:49.118Z caller=main.go:676 fs_type=19
level=info ts=2020-02-07T17:26:49.118Z caller=main.go:677 msg="TSDB started"
level=info ts=2020-02-07T17:26:49.119Z caller=main.go:747 msg="Loading configuration file" filename=prometheus.yml
level=info ts=2020-02-07T17:26:49.143Z caller=main.go:775 msg="Completed loading of configuration file" filename=prometheus.yml
level=info ts=2020-02-07T17:26:49.143Z caller=main.go:630 msg="Server is ready to receive web requests."

Now the “node” exporter will show up as a “target” in the prometheus UI (localhost:9100)

Let’s just see if we can get a pretty line chart of cpu usage. By hitting the link labeled “http://localhost:9100/metrics” we will see all of the things that the node exporter collects and stores.

There’s a likely looking metric labeled node_cpu_seconds_total{cpu=”0″,mode=”user”} which seems like it might fit the bill.
Let’s try to create a chart using that metric. Simply hit the link labeled “Graph”

Then paste the metric into the box and hit execute

Then hit “Graph”

You will see a line that just rises, which seems odd for a CPU line chart. What’s happening here is that prometheus is just scraping the total CPU seconds spent in user-mode since boot and storing that value, so of course it always goes up.

What we are used to seeing in a CPU chart is the fraction of time the CPU spent in “user-mode” over time. For instance If I sample every 10 seconds, and every 10 seconds the value of “node_cpu_seconds_total{cpu=”0”,mode=”user} increases by say “5” then I can say that the rate is 5s/10s or 50% (.5)

So to turn this line into something more familiar I use the “irate” function to turn the ever-increasing value of node_cpu_seconds_total into a rate.

Now this chart is showing rate, as sampled every 30s

So, now I have a chart in prometheus that looks sort-of like my chart from activity monitor, albeit on a different timescale. The prometheus chart is showing the user time ratio sampled every 30s whereas the activity monitor samples every 5 seconds. However, if I change the sampling in prometheus down to 5 seconds in the querey box I get this.

No Data points found.

So we need to bump up the scrape frequency

global:
scrape_interval: 5s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
evaluation_interval: 5s # Evaluate rules every 15 seconds. The default is every 1 minute.
# scrape_timeout is set to the global default (10s)

Trying to go down to 5s still gives me no datapoints found, even though my scrape interval is 5 seconds. Let’s change the scrape interval to 1second

my global config
global:
scrape_interval: 1s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
evaluation_interval: 1s # Evaluate rules every 15 seconds. The default is every 1 minute.
# scrape_timeout is set to the global default (10s)

Even scraping at 1second, I cannot use “1s” in the query. It looks like the query has to be at least 2x the scrape interval specified.

Where’s the data?

One of the great things about prometheus is that it’s more than just real-time monitoring. The scraped data is being stored in a database.

The database by default is stored in the “data” subdirectory You can use the “tsdb” binary (in the same directory as “prometheus” binary itself) to get info

$ ./tsdb ls data
BLOCK ULID MIN TIME MAX TIME NUM SAMPLES NUM CHUNKS NUM SERIES
01E0GB1T9R7FPTD894DJPHKKM8 1580947174039 1580961600000 39281 1838 776
01E0GGBWD1E1GTN796EXRBRKKX 1580990400000 1581012000000 190991 2912 728
01E0GGBSXTYYQ2RR68J8VHS60M 1581091200000 1581098400000 100358 784 784



SQL Server uses only one NUMA Node with HammerDB

Some versions of HammerDB (e.g. 3.2) may induce imbalanced NUMA utilization with SQL Server.

This can easily be observed with Resource monitor. When NUMA imbalance occurs one of the NUMA nodes will show much larger utilization than the other. E.g.

Imbalanced NUMA usage by SQL Server.

The cause and fix is well documented on this blog. In short HammerDB issues a short lived connection, for every persistent connection. This causes the SQL Server Round-robin allocation to send all the persistent worker threads to a single NUMA Node! To resolve this issue, simply comment out line #212 in the driver script.

Comment out this line to work-around the HammerDB NUMA imbalance problem.

If successful you will immediately see that the NUMA nodes are more balanced. Whether this results in more/better performance will depend on exactly where the bottleneck is.

Balanced NUMA usage by SQL Server

HammerDB: Avoiding bottlenecks in client.

HammerDB is a great tool for running Database benchmarks. However it is very easy to create an artificial bottleneck which will give a very poor benchmark result.

When setting up HammerDB to run against even a moderate modern server, it is important to avoid displaying the client transaction outputs in the HammerDB UI.

In my case just making this simple changed increased my HammerDB results by over 6X. The reason is that HammerDB spends more time updating its own UI, than it does sending transactions to the DB. When I run HammerDB, I select “Log Output to Temp” and “Use Unique Log Name”.

  • Either:
    • Un-check the “Show results” mark.
    • Or Ensure that the results are logged to file, not displayed on screen
  • Otherwise the workload generator will become the bottleneck.
Checked -> 124,000 tpm
Un-Checked -> 800,000 tpm<
/pre>



Show Output "Checked"
Show Output "Un-Checked"
HammerDB with "Show results" unchecked. SQL Server uses all the CPU (99%)
HammerDB with "Show results" checked. SQL Servr using ~20% HammerDB driver (Identified as wish86t) is using 99% of one CPU.