Endpoint Central Patches Have Failed To Download In The Server. Retry Attempts Exhausted
Mike Henderson
Once you have selected Standard Reconnection, you can change the defaults for Frequency and Reconnection Attempts, and you can check the Reconnect Forever option so that the connector or endpoint keeps trying to connect until it succeeds. Be warned, however, that large (or infinite) numbers of closely spaced reconnection attempts can consume significant resources and generate extremely long log files.
In Ansible 2.9 and later, the ansible.netcommon.network_cli connection plugin configuration option is added to controlthe number of attempts to connect to a remote host. The default number of attempts is three.After every retry attempt the delay between retries is increased by power of 2 in seconds until either themaximum attempts are exhausted or either the persistent_command_timeout or persistent_connect_timeout timers are triggered.
endpoint central patches have failed to download in the server. retry attempts exhausted
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In our experience messaging uses are often comparatively low-throughput, but may require low end-to-end latency and often depend on the strongdurability guarantees Kafka provides.In this domain Kafka is comparable to traditional messaging systems such as ActiveMQ orRabbitMQ.Website Activity TrackingThe original use case for Kafka was to be able to rebuild a user activity tracking pipeline as a set of real-time publish-subscribe feeds.This means site activity (page views, searches, or other actions users may take) is published to central topics with one topic per activity type.These feeds are available for subscription for a range of use cases including real-time processing, real-time monitoring, and loading into Hadoop oroffline data warehousing systems for offline processing and reporting.Activity tracking is often very high volume as many activity messages are generated for each user page view.MetricsKafka is often used for operational monitoring data.This involves aggregating statistics from distributed applications to produce centralized feeds of operational data.Log AggregationMany people use Kafka as a replacement for a log aggregation solution.Log aggregation typically collects physical log files off servers and puts them in a central place (a file server or HDFS perhaps) for processing.Kafka abstracts away the details of files and gives a cleaner abstraction of log or event data as a stream of messages.This allows for lower-latency processing and easier support for multiple data sources and distributed data consumption.In comparison to log-centric systems like Scribe or Flume, Kafka offers equally good performance, stronger durability guarantees due to replication,and much lower end-to-end latency.Stream ProcessingMany users of Kafka process data in processing pipelines consisting of multiple stages, where raw input data is consumed from Kafka topics and thenaggregated, enriched, or otherwise transformed into new topics for further consumption or follow-up processing.For example, a processing pipeline for recommending news articles might crawl article content from RSS feeds and publish it to an "articles" topic;further processing might normalize or deduplicate this content and publish the cleansed article content to a new topic;a final processing stage might attempt to recommend this content to users.Such processing pipelines create graphs of real-time data flows based on the individual topics.Starting in 0.10.0.0, a light-weight but powerful stream processing library called Kafka Streamsis available in Apache Kafka to perform such data processing as described above.Apart from Kafka Streams, alternative open source stream processing tools include Apache Storm andApache Samza.Event SourcingEvent sourcing is a style of application design where state changes are logged as atime-ordered sequence of records. Kafka's support for very large stored log data makes it an excellent backend for an application built in this style.Commit LogKafka can serve as a kind of external commit-log for a distributed system. The log helps replicate data between nodes and acts as a re-syncingmechanism for failed nodes to restore their data.The log compaction feature in Kafka helps support this usage.In this usage Kafka is similar to Apache BookKeeper project. 1.3 Quick Start /*Licensed to the Apache Software Foundation (ASF) under one or morecontributor license agreements. See the NOTICE file distributed withthis 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 withthe License. You may obtain a copy of the License at -2.0Unless required by applicable law or agreed to in writing, softwaredistributed 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 andlimitations under the License.*/// Define variables for doc templatesvar context= "version": "20", "dotVersion": "2.0", "fullDotVersion": "2.0.0", "scalaVersion": "2.11";This tutorial assumes you are starting fresh and have no existing Kafka or ZooKeeper data.Since Kafka console scripts are different for Unix-based and Windows platforms, on Windows platforms use bin\windows\ instead of bin/, and change the script extension to .bat.
Further, when in read_committed the seekToEnd method will return the LSOstringread_uncommitted[read_committed, read_uncommitted]mediummax.poll.interval.msThe maximum delay between invocations of poll() when using consumer group management. This places an upper bound on the amount of time that the consumer can be idle before fetching more records. If poll() is not called before expiration of this timeout, then the consumer is considered failed and the group will rebalance in order to reassign the partitions to another member. int300000[1,...]mediummax.poll.recordsThe maximum number of records returned in a single call to poll().int500[1,...]mediumpartition.assignment.strategyThe class name of the partition assignment strategy that the client will use to distribute partition ownership amongst consumer instances when group management is usedlistclass org.apache.kafka.clients.consumer.RangeAssignororg.apache.kafka.common.config.ConfigDef$NonNullV...@5622fdfmediumreceive.buffer.bytesThe size of the TCP receive buffer (SO_RCVBUF) to use when reading data. If the value is -1, the OS default will be used.int65536[-1,...]mediumrequest.timeout.msThe configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted.int30000[0,...]mediumsasl.client.callback.handler.classThe fully qualified name of a SASL client callback handler class that implements the AuthenticateCallbackHandler interface.classnullmediumsasl.jaas.configJAAS login context parameters for SASL connections in the format used by JAAS configuration files. JAAS configuration file format is described here. The format for the value is: 'loginModuleClass controlFlag (optionName=optionValue)*;'. For brokers, the config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.jaas.config=com.example.ScramLoginModule required;passwordnullmediumsasl.kerberos.service.nameThe Kerberos principal name that Kafka runs as. This can be defined either in Kafka's JAAS config or in Kafka's config.stringnullmediumsasl.login.callback.handler.classThe fully qualified name of a SASL login callback handler class that implements the AuthenticateCallbackHandler interface. For brokers, login callback handler config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.login.callback.handler.class=com.example.CustomScramLoginCallbackHandlerclassnullmediumsasl.login.classThe fully qualified name of a class that implements the Login interface. For brokers, login config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.login.class=com.example.CustomScramLoginclassnullmediumsasl.mechanismSASL mechanism used for client connections. This may be any mechanism for which a security provider is available. GSSAPI is the default mechanism.stringGSSAPImediumsecurity.protocolProtocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.stringPLAINTEXTmediumsend.buffer.bytesThe size of the TCP send buffer (SO_SNDBUF) to use when sending data. If the value is -1, the OS default will be used.int131072[-1,...]mediumssl.enabled.protocolsThe list of protocols enabled for SSL connections.listTLSv1.2,TLSv1.1,TLSv1mediumssl.keystore.typeThe file format of the key store file. This is optional for client.stringJKSmediumssl.protocolThe SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.stringTLSmediumssl.providerThe name of the security provider used for SSL connections. Default value is the default security provider of the JVM.stringnullmediumssl.truststore.typeThe file format of the trust store file.stringJKSmediumauto.commit.interval.msThe frequency in milliseconds that the consumer offsets are auto-committed to Kafka if enable.auto.commit is set to true.int5000[0,...]lowcheck.crcsAutomatically check the CRC32 of the records consumed. This ensures no on-the-wire or on-disk corruption to the messages occurred. This check adds some overhead, so it may be disabled in cases seeking extreme performance.booleantruelowclient.idAn id string to pass to the server when making requests. The purpose of this is to be able to track the source of requests beyond just ip/port by allowing a logical application name to be included in server-side request logging.string""lowfetch.max.wait.msThe maximum amount of time the server will block before answering the fetch request if there isn't sufficient data to immediately satisfy the requirement given by fetch.min.bytes.int500[0,...]lowinterceptor.classesA list of classes to use as interceptors. Implementing the org.apache.kafka.clients.consumer.ConsumerInterceptor interface allows you to intercept (and possibly mutate) records received by the consumer. By default, there are no interceptors.list""org.apache.kafka.common.config.ConfigDef$NonNullV...@4883b407lowmetadata.max.age.msThe period of time in milliseconds after which we force a refresh of metadata even if we haven't seen any partition leadership changes to proactively discover any new brokers or partitions.long300000[0,...]lowmetric.reportersA list of classes to use as metrics reporters. Implementing the org.apache.kafka.common.metrics.MetricsReporter interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.list""org.apache.kafka.common.config.ConfigDef$NonNullV...@7d9d1a19lowmetrics.num.samplesThe number of samples maintained to compute metrics.int2[1,...]lowmetrics.recording.levelThe highest recording level for metrics.stringINFO[INFO, DEBUG]lowmetrics.sample.window.msThe window of time a metrics sample is computed over.long30000[0,...]lowreconnect.backoff.max.msThe maximum amount of time in milliseconds to wait when reconnecting to a broker that has repeatedly failed to connect. If provided, the backoff per host will increase exponentially for each consecutive connection failure, up to this maximum. After calculating the backoff increase, 20% random jitter is added to avoid connection storms.long1000[0,...]lowreconnect.backoff.msThe base amount of time to wait before attempting to reconnect to a given host. This avoids repeatedly connecting to a host in a tight loop. This backoff applies to all connection attempts by the client to a broker.long50[0,...]lowretry.backoff.msThe amount of time to wait before attempting to retry a failed request to a given topic partition. This avoids repeatedly sending requests in a tight loop under some failure scenarios.long100[0,...]lowsasl.kerberos.kinit.cmdKerberos kinit command path.string/usr/bin/kinitlowsasl.kerberos.min.time.before.reloginLogin thread sleep time between refresh attempts.long60000lowsasl.kerberos.ticket.renew.jitterPercentage of random jitter added to the renewal time.double0.05lowsasl.kerberos.ticket.renew.window.factorLogin thread will sleep until the specified window factor of time from last refresh to ticket's expiry has been reached, at which time it will try to renew the ticket.double0.8lowsasl.login.refresh.buffer.secondsThe amount of buffer time before credential expiration to maintain when refreshing a credential, in seconds. If a refresh would otherwise occur closer to expiration than the number of buffer seconds then the refresh will be moved up to maintain as much of the buffer time as possible. Legal values are between 0 and 3600 (1 hour); a default value of 300 (5 minutes) is used if no value is specified. This value and sasl.login.refresh.min.period.seconds are both ignored if their sum exceeds the remaining lifetime of a credential. Currently applies only to OAUTHBEARER.short300[0,...,3600]lowsasl.login.refresh.min.period.secondsThe desired minimum time for the login refresh thread to wait before refreshing a credential, in seconds. Legal values are between 0 and 900 (15 minutes); a default value of 60 (1 minute) is used if no value is specified. This value and sasl.login.refresh.buffer.seconds are both ignored if their sum exceeds the remaining lifetime of a credential. Currently applies only to OAUTHBEARER.short60[0,...,900]lowsasl.login.refresh.window.factorLogin refresh thread will sleep until the specified window factor relative to the credential's lifetime has been reached, at which time it will try to refresh the credential. Legal values are between 0.5 (50%) and 1.0 (100%) inclusive; a default value of 0.8 (80%) is used if no value is specified. Currently applies only to OAUTHBEARER.double0.8[0.5,...,1.0]lowsasl.login.refresh.window.jitterThe maximum amount of random jitter relative to the credential's lifetime that is added to the login refresh thread's sleep time. Legal values are between 0 and 0.25 (25%) inclusive; a default value of 0.05 (5%) is used if no value is specified. Currently applies only to OAUTHBEARER.double0.05[0.0,...,0.25]lowssl.cipher.suitesA list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. By default all the available cipher suites are supported.listnulllowssl.endpoint.identification.algorithmThe endpoint identification algorithm to validate server hostname using server certificate. stringhttpslowssl.keymanager.algorithmThe algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.stringSunX509lowssl.secure.random.implementationThe SecureRandom PRNG implementation to use for SSL cryptography operations. stringnulllowssl.trustmanager.algorithmThe algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.stringPKIXlow 3.4.2 Old Consumer Configs The essential old consumer configurations are the following:
- group.id
- zookeeper.connect