* Make the output and mailbox integrations optional extras (#883) Breaking change for the next major release: pip install parsedmarc now installs the parsing core plus a working core CLI (file, IMAP, Maildir, and mbox input; CSV/JSON, Splunk HEC, webhook, and syslog output). Everything else moves behind an extra: elastic, opensearch, kafka, s3, gelf, loganalytics, msgraph, and gmail, joining the existing postgresql extra, with an umbrella [all] that deliberately excludes postgresql (psycopg's binary wheels do not exist on every platform, so parsedmarc[all] must never fail to install there). cli.py imports the six SDK-dependent output modules behind the #884 TYPE_CHECKING/try-except guard; a configured section whose extra is missing fails fast with a ConfigurationError naming the section and the exact pip install command — including the msgraph and gmail_api mailbox sections (detected via parsedmarc.mail's placeholder classes) and postgresql (checked before the constructor so the startup retry loop does not retry a missing dependency for a minute). The Azure/kiota Graph error types fall back to never-raised sentinel classes. The Docker image installs [all,postgresql], so container users see no change. CI lint installs [build,all,postgresql]; the unit-test job installs [build,all], deliberately without postgresql so test_postgres.py's absent-psycopg arm stays exercised. The never-imported dateparser dependency is dropped in favor of declaring python-dateutil, which utils.py actually imports; pytz moves to the build extra for the one test that uses it. Verified live: a no-extras wheel install imports, parses samples, and reports the install hint for each gated section; a [all] install restores every integration; the Docker image builds with every SDK importable. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * Patch psycopg presence in the PostgreSQL CLI wiring tests CI's unit-test job deliberately installs [build,all] without the postgresql extra, so parsedmarc.cli.postgres.psycopg is None there and the new missing-extra presence check correctly made _main exit 1 before the wiring under test ran. The tests simulate the SDK being available (PostgreSQLClient is mocked at the SDK boundary), so the module-level psycopg handle is now patched present in setUp. Verified against a simulated psycopg-absent environment as well as the local full install. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * Address Copilot review: narrow guards to ModuleNotFoundError, fix docs - The optional-integration and Graph error-type import guards now catch ModuleNotFoundError instead of ImportError, so only a genuinely absent package reads as a missing extra; a broken-but-present SDK fails loudly with its real error instead of masquerading as one. The test blocker raises ModuleNotFoundError accordingly — the exact exception a missing package produces. - _missing_extra_hint docstring no longer calls every gated integration an output module (it also serves the msgraph/gmail_api mailbox sections). - Fix the pre-existing passsword typo in usage.md's kafka section; the INI key the code reads is password (cli.py _parse_config). Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * Quote extras specs in copy-paste install commands From Copilot's second review round: zsh treats an unquoted .[build,all] as a glob and fails with 'no matches found', so the commands shown in AGENTS.md, CONTRIBUTING.md, dashboards/README.md, and the bootstrap script's comment are now quoted. The CI workflows keep the unquoted form: they run under bash, which passes unmatched globs through literally. The suggestion to change the 'Choosing what to install' heading level was rejected — it is a subsection of 'Installing parsedmarc', matching the file's existing hierarchy. Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * Fix upgrade command in the changelog * Documentation review: accuracy, spelling, grammar, and clarity pass A full prose review of docs/source, README, CONTRIBUTING, and the dashboards README, with every accuracy claim verified against the code before changing it. Highlights: - usage.md: documented six missing [general] options (the CSV/JSON filename options, prettify_json, normalize_timespan_threshold_hours), the required kafka smtp_tls_topic, [imap] timeout/max_retries, and the postgresql env-var prefix; corrected the maildir_path default (None, not INBOX — cli.py Namespace defaults), the mailbox check_timeout option name, the systemd restart interval (RestartSec is 5m), and merged the duplicate silent entry; quoted every copy-paste extras spec for zsh safety. - elasticsearch.md: fixed an invalid openssl command (rsa:4096 -nodes), the dashboards filename (opensearch_dashboards.ndjson, matching the file the link serves), and assorted grammar. - davmail.md: the service-enable command now enables davmail.service (was parsedmarc.service — a copy-paste error that left DavMail unenabled), plus a view typo and DavMail capitalization. - output.md: the example schema reference is RFC 7489 Appendix C (7480 is RDAP). kibana.md: SPF relies on the SMTP envelope, not session headers (RFC 7208). dmarc.md: DKM -> DKIM. - README: the intro now also names the OpenSearch/Grafana stack, matching the feature list. CONTRIBUTING: pre-PR checks now include ruff format --check and pyright, matching CI's lint job. - dashboards/README: the service table and seed description now include the PostgreSQL backend the compose stack runs. Sample data blocks, the CLI-help mirror block, and released CHANGELOG entries were deliberately left untouched. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * Docstring review: accuracy, spelling, grammar, and clarity pass Every docstring in parsedmarc/, parsedmarc/mail/, the maps maintainer scripts, and the test suite reviewed with each claim verified against the code it documents. Text-only — no behavior changes. Highlights: - Copy-paste errors corrected: parsed_smtp_tls_reports_to_csv and splunk/loganalytics save functions described aggregate or failure reports they do not handle; LogAnalyticsException claimed to be an Elasticsearch error. - Docstring/behavior mismatches: parse_report_email's report_type enumeration omitted smtp_tls; parse_failure_report typed msg_date as str (it is datetime); strip_attachment_payloads claimed payloads are replaced with None (the key is deleted); kafkaclient's failure and SMTP TLS savers claimed per-record slicing while sending the whole list in one message (docstrings now describe reality — whether slicing was intended is flagged for follow-up); the postgres savers claimed to take parse_report_file's return value but receive the inner report dict; elastic/opensearch save functions' Raises listed only AlreadySaved. - None-as-semantic-state documented where missing (get_base_domain, get_ip_address_country), enumeration completeness fixed (get_ip_address_info's 9 result keys, maps script outputs, TSV columns), and the stale 44-industry-types count corrected to the 46 the authoritative README list defines. - Test docstrings aligned with what the tests actually assert, including two that overstated coverage of the elastic/opensearch address-list tests. - Two argparse help strings fixed: file_path now names SMTP TLS report files alongside aggregate and failure, mirrored into usage.md's CLI-help block; --offline's doubled spaces removed (rendered help unchanged). - elasticsearch.md's security claim corrected against Elastic's docs: security is enabled and auto-configured on first startup since 8.0 (not "8.7 secure mode"), so the settings are verified, not hand-written. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
7.0 KiB
Using the Kibana dashboards
The Kibana DMARC dashboards are a human-friendly way to understand the results from incoming DMARC reports.
There is no separate Kibana export — Kibana 8.x's saved-object migration
handlers accept the OpenSearch Dashboards format directly, so Kibana
users import the bundled
dashboards/opensearch/opensearch_dashboards.ndjson
in Stack Management → Saved Objects → Import. A CI check imports the
same file into a Kibana 8.x container on every change so this stays
compatible.
:::{note} The default dashboard is DMARC aggregate reports. To switch between dashboards, click on the Dashboard link on the left side menu of Kibana. :::
DMARC aggregate reports
As the name suggests, this dashboard is the best place to start reviewing your aggregate DMARC data.
Across the top of the dashboard, three pie charts display the percentage of alignment pass/fail for SPF, DKIM, and DMARC. Clicking on any chart segment will filter for that value.
:::{note} Messages should not be considered malicious just because they failed to pass DMARC, especially if you have just started collecting data. It may be a legitimate service that needs SPF and DKIM configured correctly. :::
Start by filtering the results to only show failed DKIM alignment. While DMARC passes if a message passes SPF or DKIM alignment, only DKIM alignment remains valid when a message is forwarded without changing the from address, which is often caused by a mailbox forwarding rule. This is because DKIM signatures are part of the message headers, whereas SPF relies on the SMTP envelope.
Underneath the pie charts, you can see graphs of DMARC compliance and message disposition over time.
Under the graphs you will find the most useful data tables on the dashboard. On the left, there is a list of organizations that are sending you DMARC reports. In the center, there is a list of sending servers grouped by the base domain in their reverse DNS. On the right, there is the "Message volume and DMARC compliance by from domain" table, which lists email from domains with their message volume and a percentage of those messages that passed DMARC.
By hovering your mouse over a data table value and using the magnifying glass icons, you can filter on or filter out different values. Start by looking at the Message Sources by Reverse DNS table. Find a sender that you recognize, such as an email marketing service, hover over it, and click on the plus (+) magnifying glass icon, to add a filter that only shows results for that sender. Now, look at the Message volume and DMARC compliance by from domain table to the right. That shows you the domains that a sender is sending as, and what share of that traffic is passing DMARC, which might tell you which brand/business is using a particular service. With that information, you can contact them and have them set up DKIM.
:::{note}
The "Message volume and DMARC compliance by from domain" table is a TSVB
visualization, used because per-domain compliance percentages require a
Filter Ratio metric that agg-based data tables can't compute. It renders
correctly on Kibana 8.x as imported, but editing it requires first enabling
the metrics:allowStringIndices advanced setting, since it references the
dmarc_aggregate* index as a string pattern, which Elastic has deprecated.
:::
:::{note} If you have a lot of B2C customers, you may see a high volume of emails as your domains coming from consumer email services, such as Google/Gmail and Yahoo! This occurs when customers have mailbox rules in place that forward emails from an old account to a new account, which is why DKIM authentication is so important, as mentioned earlier. Similar patterns may be observed with businesses who send from reverse DNS addresses of parent, subsidiary, and outdated brands. :::
Further down the dashboard, you can filter by source country or source IP address.
Tables showing SPF and DKIM alignment details are located under the IP address
table. Each row of the DKIM details table is one real DKIM signature, shown
as a combined selector / domain / result value; the SPF details table
shows scope / domain / result the same way. Combining the values into one
column keeps each signature's selector, domain, and result paired together,
rather than aggregating them as separate columns. Because a message that
carries multiple DKIM signatures appears once per signature, summing the
messages column across rows can exceed the total number of messages.
The "Auth result filters" panel above the details tables
provides dropdowns for the individual auth-result components — DKIM
selector, DKIM domain, DKIM result, SPF scope, SPF domain, and SPF
result — and filters the whole dashboard by them. Because components from
different signatures of the same message are indexed together, combining
two of these component filters matches documents where any signature
satisfies each condition individually, not necessarily the same signature;
the combined selector / domain / result (scope / domain / result)
column remains the per-signature source of truth.
:::{note} The alignment tables (SPF details, DKIM details) and the per-IP source table live on the same dashboard, further down. To view failures only, use the pie chart at the top of the page as a filter. :::
Any other filters work the same way. You can also add your own custom temporary filters by clicking on Add Filter at the upper right of the page.
DMARC failure reports
The DMARC failure reports dashboard (formerly DMARC Forensic Samples) contains information on DMARC failure reports (also known as forensic or ruf reports). These reports contain samples of emails that have failed to pass DMARC.
:::{note} Most recipients do not send failure/ruf reports at all to avoid privacy leaks. Some recipients (notably Chinese webmail services) will only supply the headers of sample emails. Very few provide the entire email. :::
SMTP TLS reporting
The SMTP TLS reporting dashboard surfaces aggregate counts of TLS-RPT reporting organizations, the policy domains they report on, and the specific failure types — certificate expiry, STARTTLS not supported, STS policy fetch errors, validation failures, and similar — together with the sending and receiving MTA addresses involved.
Like the DKIM and SPF details tables above, the "SMTP TLS domains" and
"SMTP TLS failure details" tables show one row per policy and one row per
failure detail, respectively, using combined policy (domain / type) and
failure detail (domain / type / result / sending mta / receiving ip / mx)
columns so that each policy's or failure detail's fields stay paired
together, rather than aggregating them as separate columns. The
successful_sessions and failed_sessions columns are summed per report
document, though, not per policy: when a single report carries multiple
policies, a row's session sums include the sibling policies from that
report as well as its own. Fully attributing session counts to a single
policy would require restructuring the stored documents.