我正在尝试为我的用例启用 RocksDB 检查点，但不断收到以下错误。
AsynchronousException{java.lang.Exception: Could not materialize checkpoint 1 for operator Map -> Sink: Unnamed (1/1).}
at org.apache.flink.streaming.runtime.tasks.StreamTask$AsyncCheckpointExceptionHandler.tryHandleCheckpointException(StreamTask.java:1153)
at org.apache.flink.streaming.runtime.tasks.StreamTask$AsyncCheckpointRunnable.handleExecutionException(StreamTask.java:947)
at org.apache.flink.streaming.runtime.tasks.StreamTask$AsyncCheckpointRunnable.run(StreamTask.java:884)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.Exception: Could not materialize checkpoint 1 for operator Map -> Sink: Unnamed (1/1).
at org.apache.flink.streaming.runtime.tasks.StreamTask$AsyncCheckpointRunnable.handleExecutionException(StreamTask.java:942)
... 6 more
Caused by: java.util.concurrent.ExecutionException: java.lang.AbstractMethodError
at java.util.concurrent.FutureTask.report(FutureTask.java:122)
at java.util.concurrent.FutureTask.get(FutureTask.java:192)
at org.apache.flink.runtime.concurrent.FutureUtils.runIfNotDoneAndGet(FutureUtils.java:394)
at org.apache.flink.streaming.api.operators.OperatorSnapshotFinalizer.<init>(OperatorSnapshotFinalizer.java:53)
at org.apache.flink.streaming.runtime.tasks.StreamTask$AsyncCheckpointRunnable.run(StreamTask.java:853)
... 5 more
Caused by: java.lang.AbstractMethodError
at org.apache.flink.api.common.typeutils.TypeSerializerUtils.snapshotBackwardsCompatible(TypeSerializerUtils.java:49)
at java.util.stream.ReferencePipeline$3$1.accept(ReferencePipeline.java:193)
at java.util.Spliterators$ArraySpliterator.forEachRemaining(Spliterators.java:948)
at java.util.stream.AbstractPipeline.copyInto(AbstractPipeline.java:482)
at java.util.stream.AbstractPipeline.wrapAndCopyInto(AbstractPipeline.java:472)
at java.util.stream.AbstractPipeline.evaluate(AbstractPipeline.java:546)
at java.util.stream.AbstractPipeline.evaluateToArrayNode(AbstractPipeline.java:260)
at java.util.stream.ReferencePipeline.toArray(ReferencePipeline.java:438)
at org.apache.flink.api.common.typeutils.TypeSerializerUtils.snapshotBackwardsCompatible(TypeSerializerUtils.java:39)
at org.apache.flink.api.common.typeutils.NestedSerializersSnapshotDelegate.<init>(NestedSerializersSnapshotDelegate.java:63)
at org.apache.flink.api.common.typeutils.CompositeTypeSerializerSnapshot.<init>(CompositeTypeSerializerSnapshot.java:127)
at org.apache.flink.streaming.api.functions.sink.TwoPhaseCommitSinkFunction$StateSerializerSnapshot.<init>(TwoPhaseCommitSinkFunction.java:851)
at org.apache.flink.streaming.api.functions.sink.TwoPhaseCommitSinkFunction$StateSerializer.snapshotConfiguration(TwoPhaseCommitSinkFunction.java:785)
at org.apache.flink.streaming.api.functions.sink.TwoPhaseCommitSinkFunction$StateSerializer.snapshotConfiguration(TwoPhaseCommitSinkFunction.java:613)
at org.apache.flink.runtime.state.RegisteredOperatorStateBackendMetaInfo.computeSnapshot(RegisteredOperatorStateBackendMetaInfo.java:170)
at org.apache.flink.runtime.state.RegisteredOperatorStateBackendMetaInfo.snapshot(RegisteredOperatorStateBackendMetaInfo.java:103)
at org.apache.flink.runtime.state.DefaultOperatorStateBackendSnapshotStrategy$1.callInternal(DefaultOperatorStateBackendSnapshotStrategy.java:123)
at org.apache.flink.runtime.state.DefaultOperatorStateBackendSnapshotStrategy$1.callInternal(DefaultOperatorStateBackendSnapshotStrategy.java:108)
at org.apache.flink.runtime.state.AsyncSnapshotCallable.call(AsyncSnapshotCallable.java:75)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at org.apache.flink.runtime.concurrent.FutureUtils.runIfNotDoneAndGet(FutureUtils.java:391)
... 7 more
flink-config.yaml 是
################################################################################
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
################################################################################
#==============================================================================
# Common
#==============================================================================
# The external address of the host on which the JobManager runs and can be
# reached by the TaskManagers and any clients which want to connect. This setting
# is only used in Standalone mode and may be overwritten on the JobManager side
# by specifying the --host <hostname> parameter of the bin/jobmanager.sh executable.
# In high availability mode, if you use the bin/start-cluster.sh script and setup
# the conf/masters file, this will be taken care of automatically. Yarn/Mesos
# automatically configure the host name based on the hostname of the node where the
# JobManager runs.
jobmanager.rpc.address: fl-service
# The RPC port where the JobManager is reachable.
jobmanager.rpc.port: 6123
# The heap size for the JobManager JVM
jobmanager.heap.size: 1024m
# The heap size for the TaskManager JVM
taskmanager.heap.size: 1024m
# The number of task slots that each TaskManager offers. Each slot runs one parallel pipeline.
taskmanager.numberOfTaskSlots: 1
# The parallelism used for programs that did not specify and other parallelism.
parallelism.default: 1
# The default file system scheme and authority.
#
# By default file paths without scheme are interpreted relative to the local
# root file system 'file:///'. Use this to override the default and interpret
# relative paths relative to a different file system,
# for example 'hdfs://mynamenode:12345'
#
# fs.default-scheme
#==============================================================================
# High Availability
#==============================================================================
# The high-availability mode. Possible options are 'NONE' or 'zookeeper'.
#
high-availability: zookeeper
# The path where metadata for master recovery is persisted. While ZooKeeper stores
# the small ground truth for checkpoint and leader election, this location stores
# the larger objects, like persisted dataflow graphs.
#
# Must be a durable file system that is accessible from all nodes
# (like HDFS, S3, Ceph, nfs, ...)
#
high-availability.storageDir: file:///opt/flink/ha-storage
# The list of ZooKeeper quorum peers that coordinate the high-availability
# setup. This must be a list of the form:
# "host1:clientPort,host2:clientPort,..." (default clientPort: 2181)
#
# Since K8s automatically load balances to an active node, using the service works fine
high-availability.zookeeper.quorum: zk-service:2181
# ACL options are based on https://zookeeper.apache.org/doc/r3.1.2/zookeeperProgrammers.html#sc_BuiltinACLSchemes
# It can be either "creator" (ZOO_CREATE_ALL_ACL) or "open" (ZOO_OPEN_ACL_UNSAFE)
# The default value is "open" and it can be changed to "creator" if ZK security is enabled
#
# high-availability.zookeeper.client.acl: open
# Other options recomended by https://ci.apache.org/projects/flink/flink-docs-release-1.7/ops/jobmanager_high_availability.html#config-file-flink-confyaml
high-availability.zookeeper.path.root: /flink
high-availability.cluster-id: /cluster_one
high-availability.jobmanager.port: 50010
#==============================================================================
# Fault tolerance and checkpointing
#==============================================================================
# The backend that will be used to store operator state checkpoints if
# checkpointing is enabled.
#
# Supported backends are 'jobmanager', 'filesystem', 'rocksdb', or the
# <class-name-of-factory>.
#
state.backend: rocksdb
# Directory for checkpoints filesystem, when using any of the default bundled
# state backends.
#
state.checkpoints.dir: file:///opt/flink/checkpoints
# Default target directory for savepoints, optional.
#
state.savepoints.dir: file:///opt/flink/savepoints
# Flag to enable/disable incremental checkpoints for backends that
# support incremental checkpoints (like the RocksDB state backend).
#
state.backend.incremental: true
state.checkpoints.num-retained: 2
#==============================================================================
# Web Frontend
#==============================================================================
# The address under which the web-based runtime monitor listens.
#
#web.address: 0.0.0.0
# The port under which the web-based runtime monitor listens.
# A value of -1 deactivates the web server.
rest.port: 8081
# Flag to specify whether job submission is enabled from the web-based
# runtime monitor. Uncomment to disable.
#web.submit.enable: false
web.upload.dir: /opt/flink/web-uploads
#==============================================================================
# Advanced
#==============================================================================
# Override the directories for temporary files. If not specified, the
# system-specific Java temporary directory (java.io.tmpdir property) is taken.
#
# For framework setups on Yarn or Mesos, Flink will automatically pick up the
# containers' temp directories without any need for configuration.
#
# Add a delimited list for multiple directories, using the system directory
# delimiter (colon ':' on unix) or a comma, e.g.:
# /data1/tmp:/data2/tmp:/data3/tmp
#
# Note: Each directory entry is read from and written to by a different I/O
# thread. You can include the same directory multiple times in order to create
# multiple I/O threads against that directory. This is for example relevant for
# high-throughput RAIDs.
#
# io.tmp.dirs: /tmp
# Specify whether TaskManager's managed memory should be allocated when starting
# up (true) or when memory is requested.
#
# We recommend to set this value to 'true' only in setups for pure batch
# processing (DataSet API). Streaming setups currently do not use the TaskManager's
# managed memory: The 'rocksdb' state backend uses RocksDB's own memory management,
# while the 'memory' and 'filesystem' backends explicitly keep data as objects
# to save on serialization cost.
#
# taskmanager.memory.preallocate: false
# The classloading resolve order. Possible values are 'child-first' (Flink's default)
# and 'parent-first' (Java's default).
#
# Child first classloading allows users to use different dependency/library
# versions in their application than those in the classpath. Switching back
# to 'parent-first' may help with debugging dependency issues.
#
# classloader.resolve-order: child-first
# The amount of memory going to the network stack. These numbers usually need
# no tuning. Adjusting them may be necessary in case of an "Insufficient number
# of network buffers" error. The default min is 64MB, teh default max is 1GB.
#
# taskmanager.network.memory.fraction: 0.1
# taskmanager.network.memory.min: 64mb
# taskmanager.network.memory.max: 1gb
#==============================================================================
# Flink Cluster Security Configuration
#==============================================================================
# Kerberos authentication for various components - Hadoop, ZooKeeper, and connectors -
# may be enabled in four steps:
# 1. configure the local krb5.conf file
# 2. provide Kerberos credentials (either a keytab or a ticket cache w/ kinit)
# 3. make the credentials available to various JAAS login contexts
# 4. configure the connector to use JAAS/SASL
# The below configure how Kerberos credentials are provided. A keytab will be used instead of
# a ticket cache if the keytab path and principal are set.
# security.kerberos.login.use-ticket-cache: true
# security.kerberos.login.keytab: /path/to/kerberos/keytab
# security.kerberos.login.principal: flink-user
# The configuration below defines which JAAS login contexts
# security.kerberos.login.contexts: Client,KafkaClient
#==============================================================================
# ZK Security Configuration
#==============================================================================
# Below configurations are applicable if ZK ensemble is configured for security
# Override below configuration to provide custom ZK service name if configured
# zookeeper.sasl.service-name: zookeeper
# The configuration below must match one of the values set in "security.kerberos.login.contexts"
# zookeeper.sasl.login-context-name: Client
#==============================================================================
# HistoryServer
#==============================================================================
# The HistoryServer is started and stopped via bin/historyserver.sh (start|stop)
# Directory to upload completed jobs to. Add this directory to the list of
# monitored directories of the HistoryServer as well (see below).
#jobmanager.archive.fs.dir: hdfs:///completed-jobs/
# The address under which the web-based HistoryServer listens.
#historyserver.web.address: 0.0.0.0
# The port under which the web-based HistoryServer listens.
#historyserver.web.port: 8082
# Comma separated list of directories to monitor for completed jobs.
#historyserver.archive.fs.dir: hdfs:///completed-jobs/
# Interval in milliseconds for refreshing the monitored directories.
#historyserver.archive.fs.refresh-interval: 10000
# Additional Settings
metrics.internal.query-service.port: 50011
# Other things that would be added by docker-entrypoint.sh
# if I wasn't overwriting the file.
blob.server.port: 6124
query.server.port: 6125
代码可以在 https://github.com/varnost/Corengine 时生成。如果行 #30 在 Corengine/src/main/java/io/varnost/base/LogStream.java 中，则使其工作/崩溃的唯一更改。如果我评论它工作正常，有了它，它就会崩溃。
我做错了什么才能让 RocksDB 成功进入检查点？
谢谢！
我不确定导致问题的确切原因。但是我正在为 v1.7.2 编译并在 1.8.0 上运行。当我更新我的pom.xml为flink v1.8.0构建时(因此版本匹配(，错误消失了，检查点成功完成。