Create a track for tsdb data

tsdb/README.md

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+## TSDB Track
+
+This data is anonymized monitoring data from elastic-apps designed to test
+our TSDB project. TSDB support is being actively developed in Elasticsearch
+right now so it's best to run this track only against Elasticsearch's
+master branch.
+
+TSDB needs us to be careful how we anonymize. Too much randomization and TSDB
+can no longer do its job identifying time series and metrics and rates of
+change. Too little and everyone knows all the software we run. We mostly err
+towards openness here, but with a dash of paranoia.
+
+
+### Example document
+
+```
+{
+  "@timestamp": "2021-04-28T19:45:28.222Z",
+  "kubernetes": {
+    "namespace": "namespace0",
+    "node": {"name": "gke-apps-node-name-0"},
+    "pod": {"name": "pod-name-pod-name-0"},
+    "volume": {
+      "name": "volume-0",
+      "fs": {
+        "capacity": {"bytes": 7883960320},
+        "used": {"bytes": 12288},
+        "inodes": {"used": 9, "free": 1924786, "count": 1924795},
+        "available": {"bytes": 7883948032}
+      }
+    }
+  },
+  "metricset": {"name": "volume","period": 10000},
+  "fields": {"cluster": "elastic-apps"},
+  "host": {"name": "gke-apps-host-name0"},
+  "agent": {
+    "id": "96db921d-d0a0-4d00-93b7-2b6cfc591bc3",
+    "version": "7.6.2",
+    "type": "metricbeat",
+    "ephemeral_id": "c0aee896-0c67-45e4-ba76-68fcd6ec4cde",
+    "hostname": "gke-apps-host-name-0"
+  },
+  "ecs": {"version": "1.4.0"},
+  "service": {"address": "service-address-0","type": "kubernetes"},
+  "event": {
+    "dataset": "kubernetes.volume",
+    "module": "kubernetes",
+    "duration": 132588484
+  }
+}
+```
+
+
+### Fetching new data
+
+This data comes from a private elastic-apps private k8s cluster. If you have
+access to that cluster's logging then you can update the test data by
+fetching raw, non-anonymized data from it's monitoring cluster with something
+like elastic-dump. You want one document per line.
+
+Now that you have the data, the goal is to get the anonymizer to run on the
+entire dump in one go. But, in order to do that, let's slice out parts of the
+file and make sure they can be processed first. Then we'll run them all at once.
+
+First, let's tackle the documents with `message` fields. These are super diverse
+and likely to fail on every new batch of data.
+```
+mkdir tmp
+cd tmp
+
+grep \"message\" ../data.json | split -l 100000
+for file in x*; do
+  echo $file
+  python ../tsdb/_tools/anonymize.py < $file > /dev/null && rm $file
+done
+```
+
+Some of the runs in the `for` loop are likely to fail. Those will leave files
+around so you can fix the anonymizer and retry. We try to keep as many real
+messages as we can without leaking too much information. So any new kind of
+message will fail this process. Modify the script, redacting new message types
+that come from the errors. Rerun the for loop above until it finishes without
+error.
+
+Now run the same process on documents without a message.
+
+```
+grep -v \"message\" ../data.json | split -l 100000
+for file in x*; do
+  echo $file
+  python ../tsdb/_tools/anonymize.py < $file > /dev/null && rm $file
+done
+```
+
+These are less likely to fail but there are more documents without messages
+and any new failure will require a litte thought about whether the field it
+found needs to be redacted or modified or passed through unchanged. Good luck.
+
+Once all of those finish you should run the tool on the newly aquired data
+from start to finish. You can't split the input data or the anonymizer won't
+make consistent time series ids. Do something like:
+
+```
+cd ..
+rm -rf tmp
+python tsdb/_tools/anonymize.py < data.json > documents.json
+```
+
+Once that finishes you need to generate `documents-1k.json` for easy testing:
+```
+head -n 1000 documents.json > documents-1k.json
+```
+
+### Parameters
+
+This track allows to overwrite the following parameters using `--track-params`:
+
+* `bulk_size` (default: 10000)
+* `bulk_indexing_clients` (default: 8): Number of clients that issue bulk indexing requests.
+* `ingest_percentage` (default: 100): A number between 0 and 100 that defines how much of the document corpus should be ingested.
+* `number_of_replicas` (default: 0)
+* `number_of_shards` (default: 1)
+* `force_merge_max_num_segments` (default: unset): An integer specifying the max amount of segments the force-merge operation should use.
+* `index_mode` (default: time_series): Whether to make a standard index (`standard`) or time series index (`time_series`)
+* `codec` (default: default): The codec to use compressing the index. `default` uses more space and less cpu. `best_compression` uses less space and more cpu.
+
+### License
+
+All articles that are included are licensed as CC-BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/)
+
+This data set is licensed under the same terms. Please refer to https://creativecommons.org/licenses/by-nc-nd/4.0/ for details.