#help Hello, I tried to setup a garbage collection routine based on this user manual. I have a local dev LakeFS running which uses s3 as storage. When I tried to start the GC job (spark job), I got the following error. 22/10/18 164607 INFO S3AFileSystem: Caught an AmazonServiceException com.amazonaws.services.s3.model.AmazonS3Exception: Status Code: 403, AWS Service: Amazon S3, AWS Request ID: CS2AC7CA2JJC5W3Z, AWS Error Code: null, AWS Error Message: Forbidden, S3 Extended Request ID: ADF087vKmPaF3bhLuJ9ty+mlbW6H3eIprmlbopQt/lOO5+fb4or8OP2E70lY5q9KOYksyDHaP3k= 22/10/18 164607 INFO S3AFileSystem: Error Message: Status Code: 403, AWS Service: Amazon S3, AWS Request ID: CS2AC7CA2JJC5W3Z, AWS Error Code: null, AWS Error Message: Forbidden 22/10/18 164607 INFO S3AFileSystem: HTTP Status Code: 403 22/10/18 164607 INFO S3AFileSystem: AWS Error Code: null 22/10/18 164607 INFO S3AFileSystem: Error Type: Client 22/10/18 164607 INFO S3AFileSystem: Request ID: CS2AC7CA2JJC5W3Z 22/10/18 164607 INFO S3AFileSystem: Stack com.amazonaws.services.s3.model.AmazonS3Exception: Status Code: 403, AWS Service: Amazon S3, AWS Request ID: CS2AC7CA2JJC5W3Z, AWS Error Code: null, AWS Error Message: Forbidden, S3 Extended Request ID: ADF087vKmPaF3bhLuJ9ty+mlbW6H3eIprmlbopQt/lOO5+fb4or8OP2E70lY5q9KOYksyDHaP3k= at com.amazonaws.http.AmazonHttpClient.handleErrorResponse(AmazonHttpClient.java:798) at com.amazonaws.http.AmazonHttpClient.executeHelper(AmazonHttpClient.java:421) at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:232) at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:3528) at com.amazonaws.services.s3.AmazonS3Client.getObjectMetadata(AmazonS3Client.java:976) ...

Hello LakeFS community, is there a plan to enable support for open table formats like Iceberg, Delta Lake etc? If yes, what is the tentative ETA?

Hello, LakeFS community. Is there a way to mirror a LakeFS instance in another region? Specifically, I have a LakeFS instance connected to a DynamoDB global table and an S3 bucket. And I have another LakeFS instance connected to the same DynamoDB global table. The second LakeFS instance uses the same S3 instance. But I want it to use an S3 bucket that is a bidirectional replica of the other S3 bucket. Is this possible? The obvious answer would be to specify a multi-region access point instead of an S3 bucket. But LakeFS doesn't seem to support using a multi-region access point. Am I missing something here? For example, would this architecture be unstable due to replication delays?

Hi, fixing some lakeFS docs, I've come across this format where links get a black background. any idea how to solve this? it seems like it repeats itself everywhere where there is a link inside {: .note} or

{: .note .note-info }

Hi, I am trying to run garbage collector following the docs instructions and unfortunately I cannot progress. Please note that I am not a Scala developer, so it is very likely that I made some stupid mistakes in my setup. Kindly appreciate any help 🙌 I am using Spark 3.0.1 and I installed the following dependencies

<https://repo1.maven.org/maven2/net/java/dev/jets3t/jets3t/0.9.4/jets3t-0.9.4.jar>
<https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk/1.12.178/aws-java-sdk-1.12.178.jar>
<https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/3.3.1/hadoop-aws-3.3.1.jar>
<https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.11.375/aws-java-sdk-bundle-1.11.375.jar>
<https://repo1.maven.org/maven2/io/lakefs/hadoop-lakefs-assembly/0.1.8/hadoop-lakefs-assembly-0.1.8.jar>
<https://repo1.maven.org/maven2/io/lakefs/lakefs-spark-client-301_2.12/0.5.1/lakefs-spark-client-301_2.12-0.5.1.jar>

I am running following command

spark-submit --class io.treeverse.clients.GarbageCollector \
  --packages org.apache.hadoop:hadoop-aws:3.3.1 \
  -c spark.hadoop.lakefs.api.url="https://<my-lakefs-api-url>/api/v1"  \
  -c spark.hadoop.lakefs.api.access_key="<my-lfs-access-key>" \
  -c spark.hadoop.lakefs.api.secret_key="<my-lfs-secret-key>" \
  -c spark.hadoop.fs.s3a.access.key="<my-s3-access-key>" \
  -c spark.hadoop.fs.s3a.secret.key="<my-s3-secret-key>" \
  $SPARK_HOME/jars/lakefs-spark-client-301_2.12-0.5.1.jar \
  <my-repo> eu-west-1

and I get the following error

Exception in thread "main" com.google.common.util.concurrent.ExecutionError: java.lang.NoClassDefFoundError: dev/failsafe/function/CheckedSupplier
	at com.google.common.cache.LocalCache$Segment.get(LocalCache.java:2261)
	at com.google.common.cache.LocalCache.get(LocalCache.java:4000)
	at com.google.common.cache.LocalCache$LocalManualCache.get(LocalCache.java:4789)
	at io.treeverse.clients.ApiClient$.get(ApiClient.scala:43)
	at io.treeverse.clients.GarbageCollector$.main(GarbageCollector.scala:273)
	at io.treeverse.clients.GarbageCollector.main(GarbageCollector.scala)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.base/java.lang.reflect.Method.invoke(Method.java:566)
	at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
	at <http://org.apache.spark.deploy.SparkSubmit.org|org.apache.spark.deploy.SparkSubmit.org>$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:928)
	at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:180)
	at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:203)
	at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:90)
	at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:1007)
	at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:1016)
	at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

hey folks, is there a way to create a commit without any changes, similar to git's

--allow-empty

option? we're ingesting third-party data into lakefs and making a daily commit; it would be helpful to commit even without changes so that history shows there were no new files that day

Hi I am trying to read data from a LakeFS repo. For Object Storage, I am making use of the azure blob storage. I run the following command

#Reading data

repo = "<my-repo>"

branch = "main"

dataPath = "s3a://<my-repo/main/bronze/inp_claimsk_lds_5_2020-sample.csv"

# df = spark.read.(dataPath)

df = spark.read.format("csv").load(dataPath, inferSchema = True)

And I get the following error

Py4JJavaError: An error occurred while calling o85.load.
: java.nio.file.AccessDeniedException: smartlens: org.apache.hadoop.fs.s3a.auth.NoAuthWithAWSException: No AWS Credentials provided by SimpleAWSCredentialsProvider EnvironmentVariableCredentialsProvider InstanceProfileCredentialsProvider : com.amazonaws.SdkClientException: The requested metadata is not found at <http://169.254.169.254/latest/meta-data/iam/security-credentials/>
	at org.apache.hadoop.fs.s3a.S3AUtils.translateException(S3AUtils.java:187)
	at org.apache.hadoop.fs.s3a.Invoker.once(Invoker.java:111)
	at org.apache.hadoop.fs.s3a.Invoker.lambda$retry$3(Invoker.java:265)
	at org.apache.hadoop.fs.s3a.Invoker.retryUntranslated(Invoker.java:322)
	at org.apache.hadoop.fs.s3a.Invoker.retry(Invoker.java:261)
	at org.apache.hadoop.fs.s3a.Invoker.retry(Invoker.java:236)
	at org.apache.hadoop.fs.s3a.S3AFileSystem.verifyBucketExists(S3AFileSystem.java:375)
	at org.apache.hadoop.fs.s3a.S3AFileSystem.initialize(S3AFileSystem.java:311)
	at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:3303)
	at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:124)
	at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:3352)
	at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:3320)
	at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:479)
	at org.apache.hadoop.fs.Path.getFileSystem(Path.java:361)
	at org.apache.spark.sql.execution.streaming.FileStreamSink$.hasMetadata(FileStreamSink.scala:46)
	at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:377)
	at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:325)
	at org.apache.spark.sql.DataFrameReader.$anonfun$load$3(DataFrameReader.scala:307)
	at scala.Option.getOrElse(Option.scala:189)
	at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:307)
	at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:239)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.base/java.lang.reflect.Method.invoke(Method.java:566)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.base/java.lang.Thread.run(Thread.java:829)
Caused by: org.apache.hadoop.fs.s3a.auth.NoAuthWithAWSException: No AWS Credentials provided by SimpleAWSCredentialsProvider EnvironmentVariableCredentialsProvider InstanceProfileCredentialsProvider : com.amazonaws.SdkClientException: The requested metadata is not found at <http://169.254.169.254/latest/meta-data/iam/security-credentials/>
	at org.apache.hadoop.fs.s3a.AWSCredentialProviderList.getCredentials(AWSCredentialProviderList.java:159)
	at com.amazonaws.http.AmazonHttpClient$RequestExecutor.getCredentialsFromContext(AmazonHttpClient.java:1257)
	at com.amazonaws.http.AmazonHttpClient$RequestExecutor.runBeforeRequestHandlers(AmazonHttpClient.java:833)
	at com.amazonaws.http.AmazonHttpClient$RequestExecutor.doExecute(AmazonHttpClient.java:783)
	at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeWithTimer(AmazonHttpClient.java:770)
	at com.amazonaws.http.AmazonHttpClient$RequestExecutor.execute(AmazonHttpClient.java:744)
	at com.amazonaws.http.AmazonHttpClient$RequestExecutor.access$500(AmazonHttpClient.java:704)
	at com.amazonaws.http.AmazonHttpClient$RequestExecutionBuilderImpl.execute(AmazonHttpClient.java:686)
	at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:550)
	at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:530)
	at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5227)
	at com.amazonaws.services.s3.AmazonS3Client.getBucketRegionViaHeadRequest(AmazonS3Client.java:6189)
	at com.amazonaws.services.s3.AmazonS3Client.fetchRegionFromCache(AmazonS3Client.java:6162)
	at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5211)
	at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5173)
	at com.amazonaws.services.s3.AmazonS3Client.headBucket(AmazonS3Client.java:1438)
	at com.amazonaws.services.s3.AmazonS3Client.doesBucketExist(AmazonS3Client.java:1374)
	at org.apache.hadoop.fs.s3a.S3AFileSystem.lambda$verifyBucketExists$1(S3AFileSystem.java:376)
	at org.apache.hadoop.fs.s3a.Invoker.once(Invoker.java:109)
	... 30 more
Caused by: com.amazonaws.SdkClientException: The requested metadata is not found at <http://169.254.169.254/latest/meta-data/iam/security-credentials/>
	at com.amazonaws.internal.EC2ResourceFetcher.doReadResource(EC2ResourceFetcher.java:89)
	at com.amazonaws.internal.EC2ResourceFetcher.doReadResource(EC2ResourceFetcher.java:70)
	at com.amazonaws.internal.InstanceMetadataServiceResourceFetcher.readResource(InstanceMetadataServiceResourceFetcher.java:75)
	at com.amazonaws.internal.EC2ResourceFetcher.readResource(EC2ResourceFetcher.java:66)
	at com.amazonaws.auth.InstanceMetadataServiceCredentialsFetcher.getCredentialsEndpoint(InstanceMetadataServiceCredentialsFetcher.java:58)
	at com.amazonaws.auth.InstanceMetadataServiceCredentialsFetcher.getCredentialsResponse(InstanceMetadataServiceCredentialsFetcher.java:46)
	at com.amazonaws.auth.BaseCredentialsFetcher.fetchCredentials(BaseCredentialsFetcher.java:112)
	at com.amazonaws.auth.BaseCredentialsFetcher.getCredentials(BaseCredentialsFetcher.java:68)
	at com.amazonaws.auth.InstanceProfileCredentialsProvider.getCredentials(InstanceProfileCredentialsProvider.java:165)
	at org.apache.hadoop.fs.s3a.AWSCredentialProviderList.getCredentials(AWSCredentialProviderList.java:137)
	... 48 more

Does Lakefs support ACID transactions for data stored in Iceberg format?

hello, I am having trouble getting lakefs to provision in ecs. I was able to deploy it locally however when I try to run spin up the ecs cluster it starts up and restarts in a loop. Is there anymore documentation for an ecs deployment?

Hello, can you confirm that the helm chart does not support deploying lakefs using dynamodb as database? The database type seems to be hardcoded: https://github.com/treeverse/charts/blob/master/charts/lakefs/templates/_env.tpl#L3-L4

I have another question related to using delta in combination with lakefs after reading the limitations section:

The Delta log is an auto-generated sequence of text files used to keep track of transactions on a Delta table sequentially. Writing to one Delta table from multiple lakeFS branches is possible, but note that it would result in conflicts if later attempting to merge one branch into the other. For this reason, production workflows should ideally write to a single lakeFS branch that could then be safely merged into main.

Is this also true when the table is concurrently updated by different processes, but they work on different partitions? Ideally I would like to create one branch per writer and merge into main when the write finishes the data processing.

Hi, For my use case I’m using Spark on Scala and I’m using the

LakeFSClient

class. I’ve noticed that while on python client the commits api are exposed, this doesn’t happen on the Java counterpart. Is this something missing or is there a reason for the CommitsApi not being exposed there?

Hi, I have this setting with: • local airflow • lakeFS on aws • s3 • emr serverless studio The flow is - local airflow triggers lakeFS to create a new branch to test new logic, later it starts an emr application that leverage this new branch. To make this complete for testing environment, I would like to upload my python code from local to my lakeFS installation on the cloud using airflow, or something else that can be automated in python with the same script. any idea how to go about it? I couldn't find it in the LakeFS-Airflow operators.

Hi, In version 83.3, I can create a tag whose tag URI is the same as its commit URI. In version 86.0, that gets me a 409 Conflict error. a) Is that true? b) Is that intentional? (Of course, whether it works in the latest version is more important to me than the precise history of it.) (Also, I may have been using a lakectl version slightly older than 83.3, if the change occurred in 83.3 .) FYI, if the behavior is intentional, I need to make a change, but it's not necessarily a big deal. Thanks, Chuck

hi @Iddo Avneri, Today we had a call about restoring production data, unfortunately during the call we cannot access to our storage, but in the meantime we managed to access it. Can we do a follow-up about that? Thank you in advance!

We can help

Hi, We are evaluating LakeFS for a new Data Lake on GCP. However, reading the docs I've noticed that garbage collection is not ready yet for this platform/storage.

help lakefs Hi, Does anyone have issues with Databricks since today? I cannot write dataframes on lakefs using databricks today but yesterday it works fine and I did not change anything.. I am using latest runtime (12.0) and a spark config:

spark.hadoop.fs.s3a.impl shaded.databricks.org.apache.hadoop.fs.s3a.S3AFileSystem
spark.hadoop.fs.s3a.endpoint <http://s3.eu-west-1.amazonaws.com|s3.eu-west-1.amazonaws.com>
spark.hadoop.fs.lakefs.secret.key ...
spark.hadoop.fs.lakefs.access.key ...
spark.hadoop.fs.lakefs.endpoint http://...:8000/api/v1
spark.hadoop.fs.lakefs.impl io.lakefs.LakeFSFileSystem

and

io.lakefs:hadoop-lakefs-assembly:0.1.9

installed on the cluster And when I try to write any date with spark I have the following error:

java.io.IOException: get object metadata using underlying wrapped s3 client

In the stacktrace I also see:

Caused by: java.lang.NoSuchMethodException: com.databricks.sql.acl.fs.CredentialScopeFileSystem.getWrappedFs()

I am wondering if Databricks changed something 🤔 (Note that using spark locally or python client on databricks I can upload objects so it seems really related to spark on databricks)

Hi, I faced a very strange problem with airflow-provider-lakefs: when using a LakeFS operator inside a TaskGroup, the DAG magically disappear 🪄 I detailed everything in this issue with instructions to reproduce the bug using official Airflow docker image

Hi all, I have an Azure LakeFS installation and I want to do a zero copy import from Azure Data Lake Storage Gen2 storage account. I used `lakefs ingest`as per the documentation which ingested all files in my repo. However when I try to read them using from Azure Databricks using the s3a path I get a

Missing Credential Scope

error, which I guess comes from lakeFS not being able to authenticate to the storage account where the files actually are. The

ingest

command worked and could authenticate cause I set the Azure Storage Account key in the

AZURE_STORAGE_ACCESS_KEY

environment variable locally, but how can I make LakeFS authenticate later to read the files when I need them?

Hi, Not sure if this is an issue with LakeFS, Rclone, or something on my side. I'm using rclone to copy from one lakefs instance to another, like this: rclone sync --checksum --s3-use-multipart-etag=true lakefs_<a://repo1/tag1> lakefs_<b://repo1/main/> But I'm getting an error from rclone: Failed to copy: multipart upload corrupted: Etag differ: expecting 1234567890abcdef1234567890abcdef-51 but got 1234567890abcdef1234567890abcdef In other words, it's comparing a 32-character etag, including appended chunk count, to the same etag, withOUT the appended chunk count. Of course, they don't match. This happens if the file that's being copied is above the size threshold for multipart uploads. If I drop the "--s3-use-multipart-etag=true" parameter, it doesn't fail, but I'm not sure how much validation is done on the copy. When I do "lakectl fs stat" on the file on lakefs_a, the "Checksum" includes the appended chunk count.

hi, i am working with a lakeFS python client and trying to read data from lakefs branch. lakefs client api works for creating and listing branchs. yet something with the configurations for reading from a branch with Spark is probably missing. File path to load datafram from:

main_repo_path = "<lakefs://adi-test/main/>"

This is the exception i get:

y4JJavaError: An error occurred while calling o625.load.
: java.io.IOException: statObject
	at io.lakefs.LakeFSFileSystem.getFileStatus(LakeFSFileSystem.java:731)
	at io.lakefs.LakeFSFileSystem.getFileStatus(LakeFSFileSystem.java:43)
	at org.apache.hadoop.fs.FileSystem.isDirectory(FileSystem.java:1777)
	at org.apache.spark.sql.execution.streaming.FileStreamSink$.hasMetadata(FileStreamSink.scala:59)
	at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:407)
	at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:369)
	at org.apache.spark.sql.DataFrameReader.$anonfun$load$2(DataFrameReader.scala:325)
	at scala.Option.getOrElse(Option.scala:189)
	at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:325)
	at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:238)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)
	at py4j.Gateway.invoke(Gateway.java:306)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:195)
	at py4j.ClientServerConnection.run(ClientServerConnection.java:115)
	at java.lang.Thread.run(Thread.java:750)
Caused by: io.lakefs.shaded.api.ApiException: Content type "text/html; charset=utf-8" is not supported for type: class io.lakefs.shaded.api.model.ObjectStats
	at io.lakefs.shaded.api.ApiClient.deserialize(ApiClient.java:822)
	at io.lakefs.shaded.api.ApiClient.handleResponse(ApiClient.java:1020)
	at io.lakefs.shaded.api.ApiClient.execute(ApiClient.java:944)
	at io.lakefs.shaded.api.ObjectsApi.statObjectWithHttpInfo(ObjectsApi.java:1115)
	at io.lakefs.shaded.api.ObjectsApi.statObject(ObjectsApi.java:1089)
	at io.lakefs.LakeFSFileSystem.getFileStatus(LakeFSFileSystem.java:727)

I configured Spark cluster with the same credentials and endpoint as the programmable lakeFS client. any idea what is missing?

Hi, I`m trying to make the LakeFS work in databricks but encounter the error below:

Py4JJavaError: An error occurred while calling o407.parquet.
: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class io.lakefs.LakeFSFileSystem not found

I used the hadoop fs settings as mentioned in docus `

spark.hadoop.fs.lakefs.impl io.lakefs.LakeFSFileSystem

Please 🙏, any idea how to deal with that?

Hi all! I was trying LakeFS 0.90.0 in Azure and I had issues with performance of imports of more than 500k files. I detailed the issue in GitHub: https://github.com/treeverse/lakeFS/issues/5065 Let me know if there's any additional info I can provide to help shed some light into this

Hi All! Finally started today with our little POC after getting all approvals needed

I'm trying to build lakeFS locally and getting an error in

make test

. I've logged an issue here - LMK if I can add any more details to help 🙂

I'm new to lakeFS. We have now setup lakeFS on our own server with MinIO providing the s3 interface. So far this works as far as I can tell. Now I want to read a simple iris.data file from lakeFS that was uploaded manually to the main branch. In my understanding this should be possible using the lakeFS API, following this example: https://pydocs.lakefs.io/docs/ObjectsApi.html#get_object I assum that the username/password is equivalent to the Access Key pair? I also assume that the host is the same port as used for the UI, specified in .lakectl.yaml? Unfortunately, with this setup I receive the following error message """ Exception when calling ObjectsApi->get_object: (401) Reason: Unauthorized HTTP response headers: HTTPHeaderDict({'Content-Type': 'application/json', 'X-Content-Type-Options': 'nosniff', 'X-Request-Id': '02494329-b95e-4652-98d8-5733efd05f70', 'Date': 'Fri, 27 Jan 2023 134149 GMT', 'Content-Length': '43'}) HTTP response body: {"message":"error authenticating request"} """ Any help is appreciated!

Hi, how to fork a repo just like Git ? I want to share data with others.

Hi, any chance you can help with installation in a closed network

Hi, I thought I should ask the question here before I open an issue. When I try to create a policy that already exists, a new policy gets created successfully (updated

creation_date

and status

201

). I am using same id and policy statement. This happens both when using the

lakectl

api

0.55.0

, the Golang api

v0.89.0

, and

curl

. I expected to get a

409

status code, as described in the API. Is this intended behaviour? Thanks!