You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@hive.apache.org by "Alan Gates (JIRA)" <ji...@apache.org> on 2015/06/04 23:39:38 UTC

[jira] [Commented] (HIVE-10165) Improve hive-hcatalog-streaming extensibility and support updates and deletes.

    [ https://issues.apache.org/jira/browse/HIVE-10165?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14573630#comment-14573630 ] 

Alan Gates commented on HIVE-10165:
-----------------------------------

package.html:  
 * this is excellent documentation.  We may want to move much of this into the wiki for users.
 * Compactions are done by the metastore server, not HiveServer2.
 * "Currently, when issuing queries on streaming tables, query client must set hive.input.format = org.apache.hadoop.hive.ql.io.HiveInputFormat hive.vectorized.execution.enabled = false The above client settings are a temporary requirement and the intention is to drop the need for them in the near future."  I don't believe either of those are true anymore (as of Hive 0.14).
 
LockImpl:
 * internalAcquire: Why do you recreate the connection the metastore each time through the loop?  These seems expensive.  Same comment for building the lock request.  This shouldn't change as you go through the loop.
 * internalRelease: You've built in handling for releasing locks that are not part of transactions.  When you do envision users locking something that isn't part of a transaction?  Since this is doing write operations I would assume you'll always have a transaction.

MutatorDestination: This appears to be a simple struct that records data about a table, why have it as an interface with an impl?

TransactionImpl:
 * Why do commit() and abort() release the locks?  Since these locks are part of a transaction they will always be released when the transaction is committed or aborted.

MutatorClient:
 * Why is Lock external to this class?  It seems like Lock is a component of this class.  Or do you envision users using one Lock object to manage multiple MutatorClients? 

MutatorCoordinator:
 * Comments in the class javadoc: it's origTxnId, bucketid that controls the ordering, not lastTxnId, since origTxnId is immutable.
 * In the constructor, why are you passing in CreatePartitionHelper and SequenceValidator when there's only one instance of these?
 * resetMutator, this code is closing the Mutator everytime you switch Mutators.  But if I understand correctly this is going to result in writing a footer in the ORC file.  You're going to end up with a thousand tiny stripes in your files.  That is not what you want.  You do need to make sure you don't have too many open at a time to avoids OOMs and too many file handles open errors.  But you'll need to keep a list of which ones are open and then close them on an LRU basis (or maybe pick the one with the most records since it will give you the best stripe size) as you need to open more rather than closing each one each time.  [~owen.omalley] comments?

CreationPartitionHelper:
 * createPartitionIfNotExists: Why are you running the Driver class here?  Why not call IMetaStoreClient.addPartition()?  That would be much lighter weight.

Hive doesn't currently have a deadlock detector.  ([~ekoifman] is working on fixing this as part of HIVE-9675).  The way this is written it could deadlock with other stream writers or with SQL users.  This code will eventually recover since it only tries to lock so many times and then gives up.  I'm not sure there's anything to do about this for now, but it should be documented.

> Improve hive-hcatalog-streaming extensibility and support updates and deletes.
> ------------------------------------------------------------------------------
>
>                 Key: HIVE-10165
>                 URL: https://issues.apache.org/jira/browse/HIVE-10165
>             Project: Hive
>          Issue Type: Improvement
>          Components: HCatalog
>    Affects Versions: 1.2.0
>            Reporter: Elliot West
>            Assignee: Elliot West
>              Labels: streaming_api
>         Attachments: HIVE-10165.0.patch, HIVE-10165.4.patch, HIVE-10165.5.patch, mutate-system-overview.png
>
>
> h3. Overview
> I'd like to extend the [hive-hcatalog-streaming|https://cwiki.apache.org/confluence/display/Hive/Streaming+Data+Ingest] API so that it also supports the writing of record updates and deletes in addition to the already supported inserts.
> h3. Motivation
> We have many Hadoop processes outside of Hive that merge changed facts into existing datasets. Traditionally we achieve this by: reading in a ground-truth dataset and a modified dataset, grouping by a key, sorting by a sequence and then applying a function to determine inserted, updated, and deleted rows. However, in our current scheme we must rewrite all partitions that may potentially contain changes. In practice the number of mutated records is very small when compared with the records contained in a partition. This approach results in a number of operational issues:
> * Excessive amount of write activity required for small data changes.
> * Downstream applications cannot robustly read these datasets while they are being updated.
> * Due to scale of the updates (hundreds or partitions) the scope for contention is high. 
> I believe we can address this problem by instead writing only the changed records to a Hive transactional table. This should drastically reduce the amount of data that we need to write and also provide a means for managing concurrent access to the data. Our existing merge processes can read and retain each record's {{ROW_ID}}/{{RecordIdentifier}} and pass this through to an updated form of the hive-hcatalog-streaming API which will then have the required data to perform an update or insert in a transactional manner. 
> h3. Benefits
> * Enables the creation of large-scale dataset merge processes  
> * Opens up Hive transactional functionality in an accessible manner to processes that operate outside of Hive.
> h3. Implementation
> Our changes do not break the existing API contracts. Instead our approach has been to consider the functionality offered by the existing API and our proposed API as fulfilling separate and distinct use-cases. The existing API is primarily focused on the task of continuously writing large volumes of new data into a Hive table for near-immediate analysis. Our use-case however, is concerned more with the frequent but not continuous ingestion of mutations to a Hive table from some ETL merge process. Consequently we feel it is justifiable to add our new functionality via an alternative set of public interfaces and leave the existing API as is. This keeps both APIs clean and focused at the expense of presenting additional options to potential users. Wherever possible, shared implementation concerns have been factored out into abstract base classes that are open to third-party extension. A detailed breakdown of the changes is as follows:
> * We've introduced a public {{RecordMutator}} interface whose purpose is to expose insert/update/delete operations to the user. This is a counterpart to the write-only {{RecordWriter}}. We've also factored out life-cycle methods common to these two interfaces into a super {{RecordOperationWriter}} interface.  Note that the row representation has be changed from {{byte[]}} to {{Object}}. Within our data processing jobs our records are often available in a strongly typed and decoded form such as a POJO or a Tuple object. Therefore is seems to make sense that we are able to pass this through to the {{OrcRecordUpdater}} without having to go through a {{byte[]}} encoding step. This of course still allows users to use {{byte[]}} if they wish.
> * The introduction of {{RecordMutator}} requires that insert/update/delete operations are then also exposed on a {{TransactionBatch}} type. We've done this with the introduction of a public {{MutatorTransactionBatch}} interface which is a counterpart to the write-only {{TransactionBatch}}. We've also factored out life-cycle methods common to these two interfaces into a super {{BaseTransactionBatch}} interface. 
> * Functionality that would be shared by implementations of both {{RecordWriters}} and {{RecordMutators}} has been factored out of {{AbstractRecordWriter}} into a new abstract base class {{AbstractOperationRecordWriter}}. The visibility is such that it is open to extension by third parties. The {{AbstractOperationRecordWriter}} also permits the setting of the {{AcidOutputFormat.Options#recordIdColumn()}} (defaulted to {{-1}}) which is a requirement for enabling updates and deletes. Additionally, these options are now fed an {{ObjectInspector}} via an abstract method so that a {{SerDe}} is not mandated (it was not required for our use-case). The {{AbstractRecordWriter}} is now much leaner, handling only the extraction of the {{ObjectInspector}} from the {{SerDe}}.
> * A new abstract class, {{AbstractRecordMutator}} has been introduced to act as the base of concrete {{RecordMutator}} implementations. The key functionality added by this class is a validation step on {{update}} and {{delete}} to ensure that the record specified contains a {{RecordIdentifier}}. This was added as it is not explicitly checked for elsewhere and would otherwise generate an NPE deep down in {{OrcRecordUpdater}}.
> * There are now two private transaction batch implementations: {{HiveEndPoint.TransactionBatchImpl}} and its insert/update/delete counterpart: {{HiveEndPoint.MutationTransactionBatchImpl}}. As you might expect, {{TransactionBatchImpl}} must delegate to a {{RecordWriter}} implementation whereas {{MutationTransactionBatchImpl}} must delegates to a {{RecordMutator}} implementation. Shared transaction batch functionality has been factored out into an {{AbstractTransactionBatch}} class. In the case of {{MutationTransactionBatchImpl}} we've added a check to ensure that an error occurs should a user submit multiple types of operation to the same batch as we've found that this can lead to inconsistent data being returned from the underlying table when read from Hive.
> * To enable the usage of the different transaction batch variants we've added an additional transaction batch factory method to {{StreamingConnection}} and provided a suitable implementation in {{HiveEndPoint}}. It's worth noting that {{StreamingConnection}} is the only public facing component of the API contract that contains references to both the existing writer scheme and our mutator scheme.
> Please find this changes in the attached patch: [^HIVE-10165.0.patch].



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)