## Story #8756

### Ensure replica auditor is effective

0%

**Description**

The replication auditor service is currently configured to audit all objects every 90 days. As documented in #8582, the auditor is not working correctly. While the errors being thrown that are described in that ticket seem to be limited to `pid`

s with certain characters in them, I think the whole auditor process is not keeping up with our content.

Looking at the number of objects on each member node that haven't been audited in the last 90 days, auditing is well behind (if we consider it working at all):

SELECT sm.authoritive_member_node, count(smr.guid) AS count FROM systemmetadata sm INNER JOIN smreplicationstatus smr ON sm.guid = smr.guid WHERE smr.member_node != 'urn:node:CN' AND sm.date_uploaded < (SELECT CURRENT_DATE - interval '90 days') AND smr.date_verified < (SELECT CURRENT_DATE - interval '90 days') GROUP BY sm.authoritive_member_node ORDER BY count DESC; authoritive_member_node | count -------------------------+-------- urn:node:ARCTIC | 771872 urn:node:PANGAEA | 507456 urn:node:LTER | 416339 urn:node:DRYAD | 374439 urn:node:CDL | 242115 urn:node:PISCO | 235791 urn:node:KNB | 86075 urn:node:TDAR | 75639 urn:node:NCEI | 50974 urn:node:USGS_SDC | 40290 urn:node:TERN | 31671 urn:node:ESS_DIVE | 28830 urn:node:NMEPSCOR | 16042 urn:node:GOA | 9266 urn:node:IARC | 7677 urn:node:NRDC | 6673 urn:node:TFRI | 6478 urn:node:PPBIO | 3464 urn:node:ORNLDAAC | 3328 urn:node:FEMC | 2430 urn:node:EDI | 2098 urn:node:GRIIDC | 2065 urn:node:mnTestKNB | 2010 urn:node:SANPARKS | 2008 urn:node:ONEShare | 1874 urn:node:R2R | 1787 urn:node:USGSCSAS | 1151 urn:node:EDACGSTORE | 1075 urn:node:US_MPC | 1032 urn:node:RW | 970 urn:node:KUBI | 516 urn:node:NEON | 487 urn:node:LTER_EUROPE | 343 urn:node:IOE | 279 urn:node:RGD | 273 urn:node:ESA | 272 urn:node:NKN | 218 urn:node:OTS_NDC | 126 urn:node:BCODMO | 115 urn:node:SEAD | 90 urn:node:mnTestNKN | 50 urn:node:EDORA | 28 urn:node:ONEShare.pem | 22 urn:node:CLOEBIRD | 17 urn:node:mnTestBCODMO | 11 urn:node:USANPN | 10 urn:node:mnTestTDAR | 10 urn:node:MyMemberNode | 1

The table above represents the number of un-audited objects (in the last 90 days), but I get the feeling that the auditor isn't able to audit any of the content it is charged to audit given 1) the frequency, 2) the number of threads allotted, and 3) the configured batch count (seems way too low). ~~Note that this query excludes replicated content - this is just the original objects~~ (After looking at my query again, I think the join is including all replicas - the total is 2,935,787, which is greater than the total objects in the system (2,751,136), so this query needs to be refined).

We need to evaluate the true effectiveness of the auditor. Some strategies may include: 1) looking to see if we may be in an infinite loop on processing a few `pid`

s due to the issues in #8582, 2) seeing if we can increase the batch size by increasing the total threads allocated in the executor, and 3) decide if we need to offload the process from the CNs and distribute the workload across a cluster of workers that can do the auditing faster. Needs some thought and discussion.

**Subtasks**

### History

#### #1 Updated by Chris Jones over 3 years ago

**Description**updated (diff)

#### #2 Updated by Chris Jones over 3 years ago

**Description**updated (diff)

#### #3 Updated by Chris Jones over 3 years ago

Adding some notes on evaluating audit timing:

cjones@cn-ucsb-1$ cat /var/log/dataone/replicate/*splunk* | \ cut -d" " -f2,3 | \ tr " " "T" | tr "," "." > \ audit-times.txt

```
import numpy as np
import pandas as pd
from pandas import Series
# Load audit timestamps and sort them
audit_times = np.loadtxt('/Users/cjones/audit-times.txt', dtype = 'datetime64')
audit_times = np.sort(audit_times)
# And create a pandas.Series object
audit_series = pd.Series(audit_times)
# Create a pandas.DataFrame with the following columns:
# - time: the original sorted times
# - shifted-time: the time column shifted up one cell
# - time-lag: the difference in the times (t1 minus t0)
# -time-lag-ms: the time-lag objects converted to millis as float64s
series = pd.concat([audit_series, audit_series.shift(-1)], axis = 1)
series.columns = ['time', 'shifted-time']
series['time-lag'] = series['shifted-time'] - series['time']
# series['time-lag-ms'] = series['time-lag'].astype('timedelta64[ms]')
# Show summary stats of the lag times
series['time-lag'].describe()
# Filter out the one 2 1/2 day outlier
series['time-lag'][series['time-lag'] < np.timedelta64(24, 'h')].describe()
# count 363884
# mean 0 days 00:00:08.526884
# std 0 days 00:02:31.333184
# min 0 days 00:00:00
# 25% 0 days 00:00:00.200000
# 50% 0 days 00:00:00.560000
# 75% 0 days 00:00:01.426000
# max 0 days 01:18:42.808000
# Name: time-lag, dtype: object
```