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# troubleshoot
  • h
    Hello everyone, Im trying to ingest the meta-data worlad from this example: https://datahubproject.io/docs/how/add-custom-ingestion-source/ But i have the problem to understand, how i install the package in the Datahub CLI. When i use the recipe, i get a ERROR {datahub.entrypoints:165} - Cannot open config file
    g
    m
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  • f
    Hey All, I am trying to build an emitter that will update the schema field terms,the get_aspect_v2 func throwing GMS - 404 resource not found exception e. I have validated the URN of the dataset and everything looks fine. can you suggest please?
    e
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    • 4
  • a
    Hello guys. I want to activate 'validation' button which is closed at first when datahub quickstart runs. I was following guides for great_expectations. I've finished to install acryl-datahub[great-expectations]. However, I can't find the yaml file for great_expectations/checkpoint directory. What can I do to solve this? I attached the figure which captures my situation.
    m
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  • b
    Using the new ingestion bigquery-beta version 0,9.0 (just released) I constantly get this error. This error also appeared when using the prerelease. I don't understand what the error or how to resolve it. -edit moved block of stacktrace/code to comments
    b
    d
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  • w
    Getting following error while installing pre requisites in local system bash-3.2$ helm install prerequisites datahub/datahub-prerequisites --values values.yaml Error: INSTALLATION FAILED: unable to build kubernetes objects from release manifest: unable to recognize "": no matches for kind "PodDisruptionBudget" in version "policy/v1beta1" bash-3.2$
  • w
    New to Helm so wanted to check whether we need to install all components manually before running this step
  • a
    Hi everyone! I'm getting some errors trying to use a simple add_domain and add_owner transformers. Any help would be appreciated! Please see the code and error messages in the thread.
    l
    b
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  • b
    Hey team quick question, I was looking at the entity-registry.yaml and noticed that entities listed there don't appear to have all of their potential aspects. For example, the dataset entity lists following aspects in the entity-registry.yaml:
    Copy code
    - name: dataset
    doc: Datasets represent logical or physical data assets stored or represented in various data platforms. Tables, Views, Streams are all instances of datasets.
    category: core
    keyAspect: datasetKey
    aspects:
      - viewProperties
      - subTypes
      - datasetProfile
      - datasetUsageStatistics
      - operation
      - domains
      - schemaMetadata
      - status
      - container
      - deprecation
      - testResults
    But looking in the PDL files and mysql I know dataset entities can also have an upstreamLineage aspect for example. I'm wondering why some aspects get listed in the entity-registry yaml and osters do not and why? Thanks!
  • a
    Hi. I want to make lineages about three tables 'account_clipboard_history', 'account_fee_history', 'account_history_usd' tables shown in the first figure. Since those are postrgresdb, I made 'file_lineage.yml' first and recipe yaml file about it next. Those are shown in the second/third figure each. However, I got the error message in the last figure. It says : can't connect 192.168.5.11:3128. That's because my desktop can't connect to others easily because our company should follow network separation for computer having database. Then, how can I solve this? Also, I am not sure whether yaml file is written correctly since I'm new to do this.
    w
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    • 6
  • b
    Hello datahub people, I hope you guys are having great Friday - time to ruin it with one more weird question 😄 I wanted to ask if you have some example on how can I use the "AddDatasetProperties" class in order to update a properties for an already existing urn, is there some example on your github page that I can refer to? Thanks for a reply in an advance!
    f
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  • h
    Hello all, I am trying to load metadata from an Exasol into the Data Hub using the documentation. I am following the Meta-World example, however I have a problem which schema data I should use and how the mce object should look like. Can anyone help me here?
  • a
    hello guys, I am unable to install acryl-datahub[airflow] . I am getting this message for various dependencies:
    Copy code
    INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See <https://pip.pypa.io/warnings/backtracking> for guidance. If you want to abort this run, press Ctrl + C.
    It's been always failing, I've tried some different versions. I am using a fresh docker image of python3.10-slim. Any ideas?
    g
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  • a
    Hello, I am facing the following issue, I have asked once more a month ago or so but the problem hasn't been fixed yet. When I am trying to add terms or tags in a table, just in embeded elements, I am getting the following error:
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    "Failed to update urn:li:dataset:(urn:li:dataPlatform:dbt,iceberg.raw.eis_billingpremium_1_0,DEV) & field [version=2.0].[type=struct].[type=array].[type=struct].premium.[type=string].transaction_type. Field [version=2.0].[type=struct].[type=array].[type=struct].premium.[type=string].transaction_type does not exist in the datasets schema."
    If I try to alter an element which is on the root level, it works just fine. Any ideas?
  • w
    Copy code
    Hello guys,
I connected the superset to the Datahub to get the charts and the dashboards, but whenever I delete a chart in the superset, the Datahub still shows as if it was there even though it was deleted. Does anyone know how to solve?
  • c
    is there anyway to pass a custom config file to the jmx_exporter?
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  • c
    why does datahub-frontend's helm chart have the JMX prometheus exporter config in a configmap, but the datahub-gms chart doesn't?
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    • 1
  • m
    Good afternoon everyone, so I am facing a problem that is quite strange. The past week I did a test ingestion to reproduce how a "normal" ingestion in an production environment would be (I tried ingesting 40 tables, with one of them over 250 000 rows). The thing is that after that I have been getting some errors and the problem seemed to be that my kafka was storing a lot of info. I looked up and saw the following:
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    512     /var/volumes/kafka-broker/var/lib/kafka/data3/cleaner-offset-checkpoint
21K     /var/volumes/kafka-broker/var/lib/kafka/data3/connect_config-0
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/connect_offset-11
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/connect_offset-14
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/connect_offset-17
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/connect_offset-2
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/connect_offset-20
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/connect_offset-23
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/connect_offset-5
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/connect_offset-8
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/connect_status-2
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-0
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-1
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-10
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-11
1.5M    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-12
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-13
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-14
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-15
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-16
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-17
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-18
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-19
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-2
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-20
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-21
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-22
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-23
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-24
4.5M    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-25
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-26
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-27
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-28
12K     /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-29
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-3
8.0M    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-30
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-31
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-32
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-33
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-34
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-35
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-36
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-37
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-38
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-39
1.6M    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-4
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-40
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-41
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-42
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-43
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-44
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-45
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-46
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-47
1.6M    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-48
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-49
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-5
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-6
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-7
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-8
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/__consumer_offsets-9
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/DataHubUsageEvent_v1-0
512     /var/volumes/kafka-broker/var/lib/kafka/data3/log-start-offset-checkpoint
1.4M    /var/volumes/kafka-broker/var/lib/kafka/data3/MetadataAuditEvent_v4-0
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/MetadataChangeEvent_v4-0
583K    /var/volumes/kafka-broker/var/lib/kafka/data3/MetadataChangeLog_Timeseries_v1-0
14G     /var/volumes/kafka-broker/var/lib/kafka/data3/MetadataChangeLog_Versioned_v1-0
5.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/MetadataChangeProposal_v1-0
512     /var/volumes/kafka-broker/var/lib/kafka/data3/meta.properties
2.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/recovery-point-offset-checkpoint
2.0K    /var/volumes/kafka-broker/var/lib/kafka/data3/replication-offset-checkpoint
493K    /var/volumes/kafka-broker/var/lib/kafka/data3/_schemas-0
    Is it normal to have so much info under the topic 
    MetadataChangeLog_Versioned_v1
    ? Can something be done to control the size of the topics or erase part of the information stored in them? Note: Datahub is only used by me and no one else. This is the first time this has happened to me and I have been using Datahub for quite a few months (with small ingestions, with max of hundreds of rows) The version that I am using is the 0.8.45
    a
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  • l
    Hi all, Just noticed that an ingestion started failing post upgrade to the latest v0.9.0 (previous version was v0.8.44). Specifically a scheduled ingestion for Looker, configured via the UI. The next scheduled execution post upgrade produced an error as shown in the log below. No change to the source configuration itself had been made, for reference:
    Copy code
    source:
    type: looker
    config:
        extract_owners: true
        base_url: '<our url>'
        skip_personal_folders: true
        include_deleted: false
        client_secret: '${LOOKER_CLIENT_SECRET}'
        extract_usage_history: true
        env: prod
        platform: snowflake
        client_id: '${LOOKER_CLIENT_ID}'
pipeline_name: 'urn:li:dataHubIngestionSource:<uuid here>'
    For now I have explicitly set the CLI version on the source to 0.8.45 as a workaround - it then runs without issue. The ERROR log entry as follows:
    Copy code
    '[2022-10-15 05:03:37,869] ERROR    {datahub.entrypoints:192} - \n'
           'Traceback (most recent call last):\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/entrypoints.py", line 149, in main\n'
           '    sys.exit(datahub(standalone_mode=False, **kwargs))\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/click/core.py", line 1130, in __call__\n'
           '    return self.main(*args, **kwargs)\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/click/core.py", line 1055, in main\n'
           '    rv = self.invoke(ctx)\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/click/core.py", line 1657, in invoke\n'
           '    return _process_result(sub_ctx.command.invoke(sub_ctx))\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/click/core.py", line 1657, in invoke\n'
           '    return _process_result(sub_ctx.command.invoke(sub_ctx))\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/click/core.py", line 1404, in invoke\n'
           '    return ctx.invoke(self.callback, **ctx.params)\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/click/core.py", line 760, in invoke\n'
           '    return __callback(*args, **kwargs)\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/click/decorators.py", line 26, in new_func\n'
           '    return f(get_current_context(), *args, **kwargs)\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/telemetry/telemetry.py", line 347, in wrapper\n'
           '    raise e\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/telemetry/telemetry.py", line 299, in wrapper\n'
           '    res = func(*args, **kwargs)\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/utilities/memory_leak_detector.py", line 95, in '
           'wrapper\n'
           '    return func(ctx, *args, **kwargs)\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/cli/ingest_cli.py", line 212, in run\n'
           '    loop.run_until_complete(run_func_check_upgrade(pipeline))\n'
           '  File "/usr/local/lib/python3.10/asyncio/base_events.py", line 646, in run_until_complete\n'
           '    return future.result()\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/cli/ingest_cli.py", line 166, in '
           'run_func_check_upgrade\n'
           '    ret = await the_one_future\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/cli/ingest_cli.py", line 157, in run_pipeline_async\n'
           '    return await loop.run_in_executor(\n'
           '  File "/usr/local/lib/python3.10/concurrent/futures/thread.py", line 58, in run\n'
           '    result = self.fn(*self.args, **self.kwargs)\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/cli/ingest_cli.py", line 148, in '
           'run_pipeline_to_completion\n'
           '    raise e\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/cli/ingest_cli.py", line 134, in '
           'run_pipeline_to_completion\n'
           '    pipeline.run()\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/ingestion/run/pipeline.py", line 348, in run\n'
           '    for wu in itertools.islice(\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/ingestion/source/looker/looker_source.py", line 1164, '
           'in get_workunits\n'
           '    ) = job.result()\n'
           '  File "/usr/local/lib/python3.10/concurrent/futures/_base.py", line 451, in result\n'
           '    return self.__get_result()\n'
           '  File "/usr/local/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result\n'
           '    raise self._exception\n'
           '  File "/usr/local/lib/python3.10/concurrent/futures/thread.py", line 58, in run\n'
           '    result = self.fn(*self.args, **self.kwargs)\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/ingestion/source/looker/looker_source.py", line 1049, '
           'in process_dashboard\n'
           '    metric_dim_workunits = self.process_metrics_dimensions_and_fields_for_dashboard(\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/ingestion/source/looker/looker_source.py", line 880, '
           'in process_metrics_dimensions_and_fields_for_dashboard\n'
           '    chart_mcps = [\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/ingestion/source/looker/looker_source.py", line 881, '
           'in <listcomp>\n'
           '    self._make_metrics_dimensions_chart_mcp(element, dashboard)\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/ingestion/source/looker/looker_source.py", line 988, '
           'in _make_metrics_dimensions_chart_mcp\n'
           '    fields=self._input_fields_from_dashboard_element(dashboard_element)\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/ingestion/source/looker/looker_source.py", line 919, '
           'in _input_fields_from_dashboard_element\n'
           '    explore = self.explore_registry.get_explore(\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/ingestion/source/looker/looker_common.py", line 882, '
           'in get_explore\n'
           '    looker_explore = LookerExplore.from_api(\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/ingestion/source/looker/looker_common.py", line 602, '
           'in from_api\n'
           '    from datahub.ingestion.source.looker.lookml_source import _BASE_PROJECT_NAME\n'
           '  File "/tmp/datahub/ingest/venv-looker-0.9.0/lib/python3.10/site-packages/datahub/ingestion/source/looker/lookml_source.py", line 11, '
           'in <module>\n'
           '    import lkml\n'
           "ModuleNotFoundError: No module named 'lkml'\n"
           '[2022-10-15 05:03:37,869] ERROR    {datahub.entrypoints:195} - Command failed: \n'
           "\tNo module named 'lkml'.\n"
    d
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  • m
    Hello, in this page, the first links that appears is not functional as redirects you to a 404 page.
    e
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  • b
    Hey everyone, I'm seeing some weird behavior with glossary sub-nodes in datahub. It seems that when I create a sub-folder within a top level glossary node folder the UI displays the sub-folder name as the entire path and not just the sub-folder name. e.g. for a top-level node A with a sub-node B, the UI shows the name of the sub-node as "_A.B_". I'll include some screenshots in this thread to better demonstrate the issue, but just wanted to inquire if this is a bug or expected behavior and if there is something I should be doing differently to not see the full path as the folder name. I noticed the urn for sub-nodes also contains the full node path, so I'm not sure if that is being reflected in the UI.
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  • m
    Hi all! Question on the behavior of bulk edit. Currently if someone selects 1000 datasets of which they own 999, but one they don't (accidental selection) the bulk edit will fail for all 999 along with the 1 they don't have access. My question, is that the desired behavior or a possible issue with the current solution? Appreciate feedback (and as applicable clarification) on this question. Thanks!
    e
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  • i
    Hi all! Having trouble with MetaData auth but maybe my understanding is incorrect. I've set METADATA_SERVICE_AUTH_ENABLED on both frontend and gms but I'm still able to do a delete on /openapi/entities/v1 without a token. When I run ingestion without a token I do see errors so appears the env variable does have some affect. Shouldn't that block deletes on the endpoint I mentioned also? If not is there someway to do that? I'm on v0.8.45.
    a
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  • a
    Hi all! We are trying out the 
    column level lineage
    functionality of the latest version (
    v0.9.0
    ) and we have an issue with the new lineage visualization there. Context: We are ingesting data from Snowflake, DBT and Looker and we can see a cross-system lineage of the entities involved. Once I enable the "Show Columns" function, I can see all columns appear but I am unable to select any of the columns for most of the tables involved. It seems that column lineage can not be identified for mixed DBT/Snowflake models in this case hence I don't see more column-related edges or can not select/lock any of these columns. Are these kind of mixed models involving DBT supported currently for this feature and what is the plan on DBT side for this? Is there any related configuration that we are not taking into account? (i.e. start-time of lineage?) Also, are there any more detailed docs available about the new column-level lineage functionality? Thanks 🙂
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  • h
    Hello all, Im trying to use the SchemaMetadataClass but when im trying to use it i get this Error. Thank You!
    b
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  • f
    Business Glossary setup & maintenance for non-technical users I am setting up a process for my client, where non-technical people would be responsible for glossaries definition. So it'd be great to allow them change stuff via DataHub UI, but I want to have everything synced back to the version controlled repo (via API calls?). Simple question - is that currently possible?
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  • r
    Hi I get an error with Helm Install. Is GKE Autopilot not supported?
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    helm install prerequisites datahub/datahub-prerequisites
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    Autopilot set default resource requests for StatefulSet default/prerequisites-kafka, as resource requests were not specified. See <http://g.co/gke/autopilot-defaults.Error>: INSTALLATION FAILED: admission webhook "<http://gkepolicy.common-webhooks.networking.gke.io|gkepolicy.common-webhooks.networking.gke.io>" denied the request: GKE Policy Controller rejected the request because it violates one or more policies: {"[denied by autogke-disallow-privilege]":["container configure-sysctl is privileged; not allowed in Autopilot"]}
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    Hi all! v0.9.0 introduced a new level in navigation. On the demo site, there is a new folder which contains 1 dataset for each table. What is the purpose of this? Is there a way to delete this useless folder?
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    Hello guys! I’m setting the Datahub integration with Okta to configure SSO but I’m facing some errors during the deployment using Helm. Currently, I’m using the Datahub version 0.8.31. My current configuration into values.yaml:
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    extraEnvs:
  - name: AUTH_OIDC_ENABLED
   value: "true"
  - name: AUTH_OIDC_CLIENT_ID
   value: "<redacted>"
  - name: AUTH_OIDC_CLIENT_SECRET
   valueFrom:
    secretKeyRef:
     name: datahub-oidc-secret
     key: datahub-oidc-key
  - name: AUTH_OIDC_DISCOVERY_URI
   value: <identity_provider_host>/.well-known/openid-configuration/
  - name: AUTH_OIDC_BASE_URL
   value: <my_datahub_host>
  - name: AUTH_OIDC_SCOPE
   value: "openid profile email"
  - name: AUTH_OIDC_USER_NAME_CLAIM
   value: "email"
  - name: AUTH_OIDC_USER_NAME_CLAIM_REGEX
   value: "([^@]+)"
    All pods are fine, but when I try to access the Datahub frontend using the host configured into Ingress, I receive an internal server error (500). When I check the logs from datahub-frontend pod, I found a connection reset error. 😕 Reading the documentation, I found this configuration here: https://datahubproject.io/docs/deploy/aws/#expose-endpoints-using-a-load-balancer But, my Kubernetes cluster already have an ingress controller configured. My question is: this step (expose endpoint using a load balancer) is mandatory or I’m able to reuse the configured ingress controller from my Kubernetes cluster? Thanks for help!
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    hello. I am trying to ingest my maria db using ui interface. However, I got the error message 'pymysql.err.OperationalError : (1045, "Access denied for user \'jh.song\'@\192.168.4.79\' (using password: yes)")\n'. How to solve this?
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    Hi! all. how can i add Domain for all table/entities exists on that domain . current i had to add one by one table/ item into that domain. and i don't think this is the best way. i have policy on group and member on group can edit/add desc fields, desc of table , add domain ... had set special domain. have any rule child table have same domain with parent ? then my policy can apply for all child of parent ?
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