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https://issues.apache.org/jira/browse/HDDS-13933?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=18038778#comment-18038778
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Ivan Andika edited comment on HDDS-13933 at 11/17/25 6:52 AM:
--------------------------------------------------------------
One difficulty is that
{code:java}
AlignmentContext#isCoordinatedCall(String protocolName, String method){code}
is not compatible with OzoneManagerService, since OzoneManagerService only
contains a single RPC method (see HDDS-881 for the reason which seems to be for
unifying Ratis conversion logic)
{code:java}
/**
The OM service that takes care of Ozone namespace.
*/
service OzoneManagerService {
// A client-to-OM RPC to send client requests to OM Ratis server
rpc submitRequest(OMRequest)
returns(OMResponse);
}{code}
HDFS, on the other hand, ClientNamenodeProtocol contains a single RPC method
for each distinct RPC call
{code:java}
service ClientNamenodeProtocol {
rpc getBlockLocations(GetBlockLocationsRequestProto)
returns(GetBlockLocationsResponseProto);
rpc getServerDefaults(GetServerDefaultsRequestProto)
returns(GetServerDefaultsResponseProto);
rpc create(CreateRequestProto)returns(CreateResponseProto);
rpc append(AppendRequestProto) returns(AppendResponseProto);
... {code}
This means that for Ozone, the method will always be "submitRequest". This
means that the ReadOnly annotation in HDFS cannot be used for Ozone.
The protocolName and method is taken from RequestHeaderProto which only
contains those two fields, so we might need to extend it. However, since we
need to take a look at the parameter OMRequest.cmdType , we might need to
change the Hadoop RPC to also parse the OMRequest payload.
was (Author: JIRAUSER298977):
One difficulty is that
{code:java}
AlignmentContext#isCoordinatedCall(String protocolName, String method){code}
is not compatible with OzoneManagerService, since OzoneManagerService only
contains a single RPC method (see HDDS-881 for the reason which is for Ratis
compatibility)
{code:java}
/**
The OM service that takes care of Ozone namespace.
*/
service OzoneManagerService {
// A client-to-OM RPC to send client requests to OM Ratis server
rpc submitRequest(OMRequest)
returns(OMResponse);
}{code}
HDFS, on the other hand, ClientNamenodeProtocol contains a single RPC method
for each distinct RPC call
{code:java}
service ClientNamenodeProtocol {
rpc getBlockLocations(GetBlockLocationsRequestProto)
returns(GetBlockLocationsResponseProto);
rpc getServerDefaults(GetServerDefaultsRequestProto)
returns(GetServerDefaultsResponseProto);
rpc create(CreateRequestProto)returns(CreateResponseProto);
rpc append(AppendRequestProto) returns(AppendResponseProto);
... {code}
This means that for Ozone, the method will always be "submitRequest". This
means that the ReadOnly annotation in HDFS cannot be used for Ozone.
The protocolName and method is taken from RequestHeaderProto which only
contains those two fields, so we might need to extend it. However, since we
need to take a look at the parameter OMRequest.cmdType , we might need to
change the Hadoop RPC to also parse the OMRequest payload.
> Consistent Read from OM Followers
> ---------------------------------
>
> Key: HDDS-13933
> URL: https://issues.apache.org/jira/browse/HDDS-13933
> Project: Apache Ozone
> Issue Type: New Feature
> Reporter: Ivan Andika
> Assignee: Ivan Andika
> Priority: Major
>
> HDDS-9279 introduces the Ratis / Raft based follower reads by giving an
> option to enable Ratis Linearizable Read and Leader Lease feature. However,
> based on previous performance tests, there are significant performance
> regressions both in the client and server which makes it not production
> ready. Currently, the root cause of this performance hit has yet to be
> discovered.
> Therefore, I suggest we pursue two concurrent approaches for consistent
> follower read. We can then compare the two approach and enable the one with
> the better performance.
> * Improve the Ratis Lineariable Read and ReadIndex improvements (following
> up on HDDS-9279)
> ** In my opinion, since we are stuck on improving this, we might try the
> second approach
> * (This ticket) We follow the HDFS observer read implementation (HDFS-12943)
> ** General flow
> *** Client send an msync to OM leader and OM leader reply with the last
> applied index as part of the AlignmentContext#getLastSeenId
> *** When client send a request to the OM follower, the Hadoop RPC mechanism
> will detect the AlignmentContext and will requeue the call if the
> Call#getClientStateId() > AlignmentContext.getLastSeenStateId() (see Hadoop
> Server.Handler#run)
> **** This implies that the server RPC handler will not get blocked, unlike
> linearizable read
> ** Pros
> *** There is already a reference implementation on HDFS, we can simply
> finesse it to Ozone context
> **** e.g. FSImage#getLastAppliedOrWrittenTxId can be translated to
> StateMachine#getLastAppliedTermIndex
> *** This is similar to HDFS observer read implementation, so we know that
> this level of consistency is acceptable and we don't need a lot to prove that
> it is correct (or at least acceptable)
> **** If we know that HDFS observer read has been deployed to production with
> no consistency issue and with acceptable performance so we expect the same on
> Ozone
> *** Possible better performance since Server will requeue the call to the
> RPC server instead of blocking
> ** Cons
> *** Only supports Hadoop RPC based client (gRPC based client is not
> supported and requires its own development)
> *** Will drift the implementation away from Raft / Ratis
> ** Current Implementation plans
> *** Ozone side
> **** AlignmentContext for client
> ***** Implementation is similar to Hadoop ClientGSIContext
> **** AlignmentContext for OM
> ***** Implementation is similar to Hadoop GlobalStateIdContext
> **** Create a new proxy provider similar to ObserverReadProxyProvider to
> allow to route read requests to the OM followers
> *** Hadoop side
> **** Support parsing AlignmentContext for ProtobufRpcEngine (raised
> HADOOP-19741)
> ***** See Hadoop Server.Connection#processRequest
> Additionally I hope that implementing one can uncover issues and improvements
> on the other.
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