Date: Tue, 19 Mar 2024 08:56:13 +0000 (UTC)
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A Java API focused on mutating (insert/update/delete) records in=
to transactional tables using Hive=E2=80=99s ACID=
feature. It is introduced in Hive 2.0.0 (HIVE-10165).
Background
In certain data processing use cases it is necessary to modify existing =
data when new facts arrive. An example of this is the classic ETL merge whe=
re a copy of a data set is kept in sync with a master by the frequent appli=
cation of deltas. The deltas describe the mutations (inserts, updates, dele=
tes) that have occurred to the master since the previous sync. To implement=
such a case using Hadoop traditionally demands that the partitions contain=
ing records targeted by the mutations be rewritten. This is a coarse approa=
ch; a partition containing millions of records might be rebuilt because of =
a single record change. Additionally these partitions cannot be restated at=
omically; at some point the old partition data must be swapped with the new=
partition data. When this swap occurs, usually by issuing an HDFS rm=
followed by a mv
, the possibility exists where the dat=
a appears to be unavailable and hence any downstream jobs consuming the dat=
a might unexpectedly fail. Therefore data processing patterns that restate =
raw data on HDFS cannot operate robustly without some external mechanism to=
orchestrate concurrent access to changing data.
The availability of ACID tables in Hive provides a mechanism that both e=
nables concurrent access to data stored in HDFS (so long as it's in the ORC=
+ACID format) and also permits row level mutations on records within a tabl=
e, without the need to rewrite the existing data. But while Hive itself sup=
ports INSERT
, UPDATE
and DELETE
comm=
ands, and the ORC format can support large batches of mutations in a transa=
ction, Hive's execution engine currently submits each individual mutation o=
peration in a separate transaction and issues table scans (M/R jobs) to exe=
cute them. It does not currently scale to the demands of processing large d=
eltas in an atomic manner. Furthermore it would be advantageous to extend a=
tomic batch mutation capabilities beyond Hive by making them available to o=
ther data processing frameworks. The Streaming Mutation API does just this.=
The Streaming Mutation API, although similar to the Streaming API, has a number of d=
ifferences and is built to enable very different use cases. Superficially, =
the Streaming API can only write new data whereas the mutation API can also=
modify existing data. However the two APIs are also based on very differen=
t transaction models. The Streaming API focuses on surfacing a continuous s=
tream of new data into a Hive table and does so by batching small sets of w=
rites into multiple short-lived transactions. Conversely the mutation API i=
s designed to infrequently apply large sets of mutations to a data set in a=
n atomic fashion: either all or none of the mutations will be applied. This=
instead mandates the use of a single long-lived transaction. This table su=
mmarises the attributes of each API:
Attribute |
Streaming API |
Mutation API |
Ingest type |
Data arrives continuously. |
Ingests are performed periodically and the mutat=
ions are applied in a single batch. |
Transaction scope |
Transactions are created for small batches of wr=
ites. |
The entire set of mutations should be applied wi=
thin a single transaction. |
Data availability |
Surfaces new data to users frequently and quickl=
y. |
Change sets should be applied atomically, either=
the effect of the delta is visible or it is not. |
Sensitive to record order |
No, records do not have pre-existing lastTxnIds =
or bucketIds. Records are likely being written into a single partition (tod=
ay's date for example). |
Yes, all mutated records have existing Rec=
ordIdentifiers and must be grouped by [partitionValues, bucketId] an=
d sorted by lastTxnId. These record coordinates initially arrive in an orde=
r that is effectively random. |
Impact of a write failure |
Transaction can be aborted and producer can choo=
se to resubmit failed records as ordering is not important. |
Ingest for the respective group (partitionValues=
+ bucketId) must be halted and failed records resubmitted to preserve=
sequence. |
User perception of missing data |
Data has not arrived yet =E2=86=92 "latency?"
| "This data is inconsistent, some records have be=
en updated, but other related records have not" =E2=80=93 consider here the classic transfer between ba=
nk accounts scenario. |
API end point scope |
A given HiveEndPoint inst=
ance submits many transactions to a specific bucket, in a specific partitio=
n, of a specific table. |
A set of MutationCoordinators =
writes changes to unknown set of buckets, of an unknown set of partitions, =
of specific tables (can be more than one), within a single transaction. |
Structure
The API comprises two main concerns: transaction management, and the wri=
ting of mutation operations to the data set. The two concerns have a minima=
l coupling as it is expected that transactions will be initiated from a sin=
gle job launcher type process while the writing of mutations will be s=
caled out across any number of worker nodes. In the context of Hadoop M/R t=
hese can be more concretely defined as the Tool and Map/Reduce task compone=
nts. However, use of this architecture is not mandated and in fact both con=
cerns could be handled within a single simple process depending on the requ=
irements.
Note that a suitably configured Hive instance is required to operate thi=
s system even if you do not intend to access the data from within Hive. Int=
ernally, transactions are managed by the Hive MetaStore. Mutations are perf=
ormed to HDFS via ORC APIs that bypass the MetaStore. Additionally you may =
wish to configure your MetaStore instance to perform periodic data compacti=
ons.
Note on packaging: The APIs are defined in the or=
g.apache.hive.hcatalog.streaming.mutate
Java package and included as=
the hive-hcatalog-streaming
jar.
Data Requirements<=
/h1>
Generally speaking, to apply a mutation to a record one must have some u=
nique key that identifies the record. However, primary keys are not a const=
ruct provided by Hive. Internally Hive uses RecordIdentifiers
&=
nbsp;stored in a virtual ROW__ID
column to uniquely ident=
ify records within an ACID table. Therefore, any process that wishes t=
o issue mutations to a table via this API must have available the correspon=
ding row ids for the target records. What this means in practice is that th=
e process issuing mutations must first read in a current snapshot of the da=
ta and then join the mutations on some domain specific primary key to obtai=
n the corresponding Hive ROW__ID
. This is effectively what occ=
urs within Hive's table scan process when an UPDATE
or DELETE statement is executed. The AcidInputFormat provides access to this data via AcidRecordReader.getRecordI=
dentifier()
.
The implementation of the ACID format places some constraints on the ord=
er in which records are written and it is important that this ordering is e=
nforced. Additionally, data must be grouped appropriately to adhere to the =
constraints imposed by the OrcRecordUpdater
. Grouping als=
o makes it possible to parallelise the writing of mutations for the purpose=
s of scaling. Finally, to correctly bucket new records (inserts) there is a=
slightly unintuitive trick that must be applied.
All of these data sequencing concerns are the responsibility of the clie=
nt process calling the API which is assumed to have first class grouping an=
d sorting capabilities (Hadoop Map/Reduce etc). The streaming API provides =
nothing more than validators that fail fast when they encounter groups and =
records that are out of sequence.
In short, API client processes should prepare data for the mutate API li=
ke so:
- MUST: Order records by
ROW__ID.originalTxn
, then ROW__ID.rowId
.
- MUST: Assign a
ROW__ID
containing a =
computed bucketId
to each record to be inserted.
- SHOULD: Group/pa=
rtition by table partition value, then
ROW__ID.bucketId
.
The addition of bucket ids to insert records prior to grouping and sorti=
ng seems unintuitive. However, it is required to ensure both adequate parti=
tioning of new data and bucket allocation consistent with that provided by =
Hive. In a typical ETL the majority of mutation events are inserts, often t=
argeting a single partition (new data for the previous day, hour, etc.). If=
more that one worker is writing said events, were we to leave the bucket i=
d empty then all inserts would go to a single worker (e.g: reducer) and the=
workload could be heavily skewed. The assignment of a computed bucket allo=
ws inserts to be more usefully distributed across workers. Additionally, wh=
en Hive is working with the data it may expect records to have been buckete=
d in a way that is consistent with its own internal scheme. A convenience t=
ype and method is provided to more easily compute and append bucket ids: BucketIdResolver and BucketIdResolverImpl
.
Update operations should not attempt to modify values of partition or bu=
cketing columns. The API does not prevent this and such attempts could lead=
to data corruption.
Streaming Req=
uirements
A few things are currently required to use streaming.
- Currently, only ORC storage format is supported. So '
stored as orc
' =
must be specified during table creation.
- The Hive table must be bucketed, but not sorted. So something like 'clustered by (colName) into 10 buckets' must be specified du=
ring table creation. See Bucketed Tables for a detailed example.
- User of the client streaming process must have the necessary permission=
s to write to the table or partition and create partitions in the table.
- Hive transactions must be configured for each table (see H=
ive Transactions =E2=80=93 Table Properties) as well as =
in
hive-site.xml
(see Hive Transactions =
=E2=80=93 Configuration).
Note: Hive also supports streaming mutations to&nb=
sp;unpartitioned tables.
Record Layout
The structure, layout, and encoding of records is the exclusive concern =
of the client ETL mutation process and may be quite different from the targ=
et Hive ACID table. The mutation API requires concrete implementations of t=
he MutatorFactory
and Mutator
classes t=
o extract pertinent data from records and serialize data into the ACID file=
s. Fortunately base classes are provided (AbstractMutator
, RecordInspectorImpl) to simplify this effort and usually all that=
is required is the specification of a suitable ObjectInspector
and the provision of the indexes of the ROW__ID
an=
d bucketed columns within the record structure. Note that all column indexe=
s in these classes are with respect to your record structure, not the Hive =
table structure.
You will likely also want to use a BucketIdResolver
to=
append bucket ids to new records for insertion. Fortunately the core imple=
mentation is provided in BucketIdResolverImpl
but note th=
at bucket column indexes must be presented in the same order as they are in=
the Hive table definition to ensure consistent bucketing. Note that you ca=
nnot move records between buckets and an exception will be thrown if you at=
tempt to do so. In real terms this means that you should not attempt to mod=
ify the values in bucket columns with an UPDATE
.
=
Connection and Transaction Management
The MutatorClient
class is used to create and man=
age transactions in which mutations can be performed. The scope of a transa=
ction can extend across multiple ACID tables. When a client connects, it co=
mmunicates with the metastore to verify and acquire metadata for the target=
tables. An invocation of newTransaction
then opens =
a transaction with the metastore, finalizes a collection of Acid=
Tables
and returns a new Transaction
ins=
tance. The ACID tables are light-weight, serializable objects that are used=
by the mutation writing components of the API to target specific ACID file=
locations. Usually your MutatorClient
will be runni=
ng on some master node and your coordinators on worker nodes. In this event=
the AcidTableSerializer
can be used to encode the t=
ables in a more transportable form, for use as a Configuration=
code> property for example.
As you would expect, a Transaction
must be initia=
ted with a call to begin
before any mutations can be=
applied. This invocation acquires a lock on the targeted tables using the =
metastore, and initiates a heartbeat to prevent transaction timeouts. It is=
highly recommended that you register a LockFailureListener
&nb=
sp;with the client so that your process can handle any lock or transaction =
failures. Typically you may wish to abort the job in the event of such an e=
rror. With the transaction in place you can now start streaming mutations w=
ith one or more MutatorCoordinator
instances (more o=
n this later), and then can commit
or abo=
rt
the transaction when the change set has been applied, which =
will release the lock with the metastore client. Finally you should close the mutation client to release any held resources.
The MutatorClientBuilder
is provided to simplify =
the construction of clients.
WARNING: Hive doesn't currently have a deadlock de=
tector (it is being worked on as part of HIV=
E-9675). This API could potentially deadlock with other stream writers =
or with SQL users.
Writing Data
The MutatorCoordinator
class is used to issue mut=
ations to an ACID table. You will require at least one instance per table p=
articipating in the transaction. The target of a given instance is defined =
by the respective AcidTable
used to construct the co=
ordinator. It is recommended that a MutatorClientBuilder
=
be used to simplify the construction process.
Mutations can be applied by invoking the respective insert, update
, and delete
methods o=
n the coordinator. These methods each take as parameters the target partiti=
on of the record and the mutated record. In the case of an unpartitioned ta=
ble you should simply pass an empty list as the partition value. For insert=
s specifically, only the bucket id will be extracted from the Re=
cordIdentifier
, the transaction id and row id will be ignored and re=
placed by appropriate values in the RecordUpdater
.
Additionally, in the case of deletes, everything but the Reco=
rdIdentifier
in the record will be ignored and therefore it is =
often easier to simply submit the original record.
Caution: As mentioned previously, mutations must a=
rrive in a specific order for the resultant table data to be consisten=
t. Coordinators will verify a naturally ordered sequence of [lastTransactio=
nId, rowId] and will throw an exception if this sequence is broken. This ex=
ception should almost certainly be escalated so that the transaction is abo=
rted. This, along with the correct ordering of the data, is the responsibil=
ity of the client using the API.
Dynamic Pa=
rtition Creation
It is very likely to be desirable to have new partitions created automat=
ically (say on a hourly basis). In such cases requiring the Hive admin to p=
re-create the necessary partitions may not be reasonable. The API allows co=
ordinators to create partitions as needed (see: MutatorClientBui=
lder.addSinkTable(String, String, boolean)
). Partition creation bein=
g an atomic action, multiple coordinators can race to create the partition,=
but only one would succeed, so coordinators' clients need not synchronize =
when creating a partition. The user of the coordinator process needs to be =
given write permissions on the Hive table in order to create partitions.
Care must be taken when using this option as it requires that the coordi=
nators maintain a connection with the metastore database. When coordinators=
are running in a distributed environment (as is likely the case) it is pos=
sible for them to overwhelm the metastore. In such cases it may be better t=
o disable partition creation and collect a set of affected partitions as pa=
rt of your ETL merge process. These can then be created with a single metas=
tore connection in your client code, once the cluster side merge process is=
complete.
Finally, note that when partition creation is disabled the coordinators =
must synthesize the partition URI as they cannot retrieve it from the metas=
tore. This may cause problems if the layout of your partitions in HDFS does=
not follow the Hive standard (as implemented in org.apache.hadoop.hi=
ve.metastore.Warehouse.getPartitionPath(Path, LinkedHashMap <String, Str=
ing>)
).
Reading Data
Although this API is concerned with writing changes to data, as previous=
ly stated we'll almost certainly have to read the existing data first to ob=
tain the relevant ROW__ID
s. Therefore it is worth noting =
that reading ACID data in a robust and consistent manner requires the follo=
wing:
- Obtaining a valid transaction list from the metastore (
ValidTxnLi=
st
).
- Acquiring a lock with the metastore and issuing heartbeats (
LockI=
mpl
can help with this).
- Configuring the
OrcInputFormat
and then reading =
the data. Make sure that you also pull in the ROW__ID
&nbs=
p;values. See: AcidRecordReader.getRecordIdentifier
.
- Releasing the lock.
Example
So to recap, the sequence of events required to apply mutations to a dat=
aset using the API is:
- Create a
MutatorClient
to manage a transaction f=
or the targeted ACID tables. This set of tables should include any transact=
ional destinations or sources. Don't forget to register a LockFa=
ilureListener
so that you can handle transaction failures.
- Open a new
Transaction
with the client.
- Get the
AcidTables
from the client.
- Begin the transaction.
- Create at least one
MutatorCoordinator
for each =
table. The AcidTableSerializer
can help you transpor=
t the AcidTables
when your workers are in a distribu=
ted environment.
- Compute your mutation set (this is your ETL merge process).
- Optionally: collect the set of affected partitions.
- Append bucket ids to insertion records. A
BucketIdResolver=
code> can help here.
- Group and sort your data appropriately.
- Issue mutation events to your coordinators.
- Close your coordinators.
- Abort or commit the transaction.
- Close your mutation client.
- Optionally: create any affected partitions that do not exist in the met=
astore.
See ExampleUseCase and TestMutati=
ons.testUpdatesAndDeletes()
for some very simple usages.
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