Pop It Trading Auto Buy, Server Crasher.txt
Before you accept an app transfer for an app that offers auto-renewable subscriptions, obtain the app-specific shared secret from the initiator, so that you can update your servers to use the code to verify auto-renewable subscriptions. Once the app transfer is complete, generate an app-specific shared secret so that users outside of your organization no longer have access to it.
Pop It Trading Auto Buy, Server Crasher.txt
When you add an account to Outlook, your mail, calendar items, files, contacts, settings and other data from that account will automatically sync to your device. If you are using the mobile Outlook application, that data will also sync to Microsoft servers to enable additional features such as faster search, personalized filtering of less important mail, and an ability to add email attachments from linked file storage providers without leaving the Outlook application. If you are using the desktop Outlook application, you can choose whether to allow the data to sync to our servers. At any time, you can remove an account or make changes to the data that is synced from your account.
You may also opt in to share your language, typing data, and/or voice clips for the purposes of improving Microsoft products and services. Depending on the opt-ins you choose, SwiftKey may send short snippets of data about what and how you type and/or your voice clips, and related correction data to our servers for processing. These text snippets and/or voice clips are used in various automated processes to validate that our prediction services are working correctly and to make product improvements. To preserve your privacy, SwiftKey Services de-identify these text snippets, and even if you have a SwiftKey Account, these text snippets and/or voice clips will not be linked to it. To learn more about how Microsoft manages your voice data, see Speech recognition technologies.
If you use Movies & TV to access content that has been protected with Microsoft Digital Rights Management (DRM), it may automatically request media usage rights from an online rights server and download and install DRM updates in order to let you play the content. See the DRM information in the Silverlight section of this privacy statement for more information.
Each partition has one server which acts as the "leader" and zero or more servers which act as "followers". The leader handles all read and write requests for the partition while the followers passively replicate the leader. If the leader fails, one of the followers will automatically become the new leader. Each server acts as a leader for some of its partitions and a follower for others so load is well balanced within the cluster.
NOTE: any prefixed ACLs added to a cluster, even after the cluster is fully upgraded, will be ignored should the cluster be downgraded again. Notable changes in 2.0.0 KIP-186 increases the default offset retention time from 1 day to 7 days. This makes it less likely to "lose" offsets in an application that commits infrequently. It also increases the active set of offsets and therefore can increase memory usage on the broker. Note that the console consumer currently enables offset commit by default and can be the source of a large number of offsets which this change will now preserve for 7 days instead of 1. You can preserve the existing behavior by setting the broker config offsets.retention.minutes to 1440.
Support for Java 7 has been dropped, Java 8 is now the minimum version required.
The default value for ssl.endpoint.identification.algorithm was changed to https, which performs hostname verification (man-in-the-middle attacks are possible otherwise). Set ssl.endpoint.identification.algorithm to an empty string to restore the previous behaviour.
KAFKA-5674 extends the lower interval of max.connections.per.ip minimum to zero and therefore allows IP-based filtering of inbound connections.
KIP-272 added API version tag to the metric kafka.network:type=RequestMetrics,name=RequestsPerSec,request=FetchFollower. This metric now becomes kafka.network:type=RequestMetrics,name=RequestsPerSec,request=FetchFollower,version=.... This will impact JMX monitoring tools that do not automatically aggregate. To get the total count for a specific request type, the tool needs to be updated to aggregate across different versions.
KIP-225 changed the metric "records.lag" to use tags for topic and partition. The original version with the name format "topic-partition.records-lag" has been removed.
The Scala consumers, which have been deprecated since 0.11.0.0, have been removed. The Java consumer has been the recommended option since 0.10.0.0. Note that the Scala consumers in 1.1.0 (and older) will continue to work even if the brokers are upgraded to 2.0.0.
The Scala producers, which have been deprecated since 0.10.0.0, have been removed. The Java producer has been the recommended option since 0.9.0.0. Note that the behaviour of the default partitioner in the Java producer differs from the default partitioner in the Scala producers. Users migrating should consider configuring a custom partitioner that retains the previous behaviour. Note that the Scala producers in 1.1.0 (and older) will continue to work even if the brokers are upgraded to 2.0.0.
MirrorMaker and ConsoleConsumer no longer support the Scala consumer, they always use the Java consumer.
The ConsoleProducer no longer supports the Scala producer, it always uses the Java producer.
A number of deprecated tools that rely on the Scala clients have been removed: ReplayLogProducer, SimpleConsumerPerformance, SimpleConsumerShell, ExportZkOffsets, ImportZkOffsets, UpdateOffsetsInZK, VerifyConsumerRebalance.
The deprecated kafka.tools.ProducerPerformance has been removed, please use org.apache.kafka.tools.ProducerPerformance.
New Kafka Streams configuration parameter upgrade.from added that allows rolling bounce upgrade from older version.
KIP-284 changed the retention time for Kafka Streams repartition topics by setting its default value to Long.MAX_VALUE.
Updated ProcessorStateManager APIs in Kafka Streams for registering state stores to the processor topology. For more details please read the Streams Upgrade Guide.
In earlier releases, Connect's worker configuration required the internal.key.converter and internal.value.converter properties. In 2.0, these are no longer required and default to the JSON converter. You may safely remove these properties from your Connect standalone and distributed worker configurations: internal.key.converter=org.apache.kafka.connect.json.JsonConverter internal.key.converter.schemas.enable=false internal.value.converter=org.apache.kafka.connect.json.JsonConverter internal.value.converter.schemas.enable=false
KIP-266 adds a new consumer configuration default.api.timeout.ms to specify the default timeout to use for KafkaConsumer APIs that could block. The KIP also adds overloads for such blocking APIs to support specifying a specific timeout to use for each of them instead of using the default timeout set by default.api.timeout.ms. In particular, a new poll(Duration) API has been added which does not block for dynamic partition assignment. The old poll(long) API has been deprecated and will be removed in a future version. Overloads have also been added for other KafkaConsumer methods like partitionsFor, listTopics, offsetsForTimes, beginningOffsets, endOffsets and close that take in a Duration.
Also as part of KIP-266, the default value of request.timeout.ms has been changed to 30 seconds. The previous value was a little higher than 5 minutes to account for maximum time that a rebalance would take. Now we treat the JoinGroup request in the rebalance as a special case and use a value derived from max.poll.interval.ms for the request timeout. All other request types use the timeout defined by request.timeout.ms
The internal method kafka.admin.AdminClient.deleteRecordsBefore has been removed. Users are encouraged to migrate to org.apache.kafka.clients.admin.AdminClient.deleteRecords.
The AclCommand tool --producer convenience option uses the KIP-277 finer grained ACL on the given topic.
KIP-176 removes the --new-consumer option for all consumer based tools. This option is redundant since the new consumer is automatically used if --bootstrap-server is defined.
KIP-290 adds the ability to define ACLs on prefixed resources, e.g. any topic starting with 'foo'.
KIP-283 improves message down-conversion handling on Kafka broker, which has typically been a memory-intensive operation. The KIP adds a mechanism by which the operation becomes less memory intensive by down-converting chunks of partition data at a time which helps put an upper bound on memory consumption. With this improvement, there is a change in FetchResponse protocol behavior where the broker could send an oversized message batch towards the end of the response with an invalid offset. Such oversized messages must be ignored by consumer clients, as is done by KafkaConsumer. KIP-283 also adds new topic and broker configurations message.downconversion.enable and log.message.downconversion.enable respectively to control whether down-conversion is enabled. When disabled, broker does not perform any down-conversion and instead sends an UNSUPPORTED_VERSION error to the client.
Note: If you are willing to accept downtime, you can simply take all the brokers down, update the code and start all of them. They will start with the new protocol by default.Note: Bumping the protocol version and restarting can be done any time after the brokers were upgraded. It does not have to be immediately after.Potential breaking changes in 0.10.1.0 The log retention time is no longer based on last modified time of the log segments. Instead it will be based on the largest timestamp of the messages in a log segment.
The log rolling time is no longer depending on log segment create time. Instead it is now based on the timestamp in the messages. More specifically. if the timestamp of the first message in the segment is T, the log will be rolled out when a new message has a timestamp greater than or equal to T + log.roll.ms
The open file handlers of 0.10.0 will increase by 33% because of the addition of time index files for each segment.
The time index and offset index share the same index size configuration. Since each time index entry is 1.5x the size of offset index entry. User may need to increase log.index.size.max.bytes to avoid potential frequent log rolling.
Due to the increased number of index files, on some brokers with large amount the log segments (e.g. >15K), the log loading process during the broker startup could be longer. Based on our experiment, setting the num.recovery.threads.per.data.dir to one may reduce the log loading time.
Upgrading a 0.10.0 Kafka Streams Application Upgrading your Streams application from 0.10.0 to 0.10.1 does require a broker upgrade because a Kafka Streams 0.10.1 application can only connect to 0.10.1 brokers.
There are couple of API changes, that are not backward compatible (cf. Streams API changes in 0.10.1 for more details). Thus, you need to update and recompile your code. Just swapping the Kafka Streams library jar file will not work and will break your application.
Upgrading from 0.10.0.x to 0.10.1.2 requires two rolling bounces with config upgrade.from="0.10.0" set for first upgrade phase (cf. KIP-268). As an alternative, an offline upgrade is also possible. prepare your application instances for a rolling bounce and make sure that config upgrade.from is set to "0.10.0" for new version 0.10.1.2
bounce each instance of your application once
prepare your newly deployed 0.10.1.2 application instances for a second round of rolling bounces; make sure to remove the value for config upgrade.mode
bounce each instance of your application once more to complete the upgrade
Upgrading from 0.10.0.x to 0.10.1.0 or 0.10.1.1 requires an offline upgrade (rolling bounce upgrade is not supported) stop all old (0.10.0.x) application instances
update your code and swap old code and jar file with new code and new jar file
restart all new (0.10.1.0 or 0.10.1.1) application instances
Notable changes in 0.10.1.0 The new Java consumer is no longer in beta and we recommend it for all new development. The old Scala consumers are still supported, but they will be deprecated in the next release and will be removed in a future major release.
The --new-consumer/--new.consumer switch is no longer required to use tools like MirrorMaker and the Console Consumer with the new consumer; one simply needs to pass a Kafka broker to connect to instead of the ZooKeeper ensemble. In addition, usage of the Console Consumer with the old consumer has been deprecated and it will be removed in a future major release.
Kafka clusters can now be uniquely identified by a cluster id. It will be automatically generated when a broker is upgraded to 0.10.1.0. The cluster id is available via the kafka.server:type=KafkaServer,name=ClusterId metric and it is part of the Metadata response. Serializers, client interceptors and metric reporters can receive the cluster id by implementing the ClusterResourceListener interface.
The BrokerState "RunningAsController" (value 4) has been removed. Due to a bug, a broker would only be in this state briefly before transitioning out of it and hence the impact of the removal should be minimal. The recommended way to detect if a given broker is the controller is via the kafka.controller:type=KafkaController,name=ActiveControllerCount metric.
The new Java Consumer now allows users to search offsets by timestamp on partitions.
The new Java Consumer now supports heartbeating from a background thread. There is a new configuration max.poll.interval.ms which controls the maximum time between poll invocations before the consumer will proactively leave the group (5 minutes by default). The value of the configuration request.timeout.ms must always be larger than max.poll.interval.ms because this is the maximum time that a JoinGroup request can block on the server while the consumer is rebalancing, so we have changed its default value to just above 5 minutes. Finally, the default value of session.timeout.ms has been adjusted down to 10 seconds, and the default value of max.poll.records has been changed to 500.
When using an Authorizer and a user doesn't have Describe authorization on a topic, the broker will no longer return TOPIC_AUTHORIZATION_FAILED errors to requests since this leaks topic names. Instead, the UNKNOWN_TOPIC_OR_PARTITION error code will be returned. This may cause unexpected timeouts or delays when using the producer and consumer since Kafka clients will typically retry automatically on unknown topic errors. You should consult the client logs if you suspect this could be happening.
Fetch responses have a size limit by default (50 MB for consumers and 10 MB for replication). The existing per partition limits also apply (1 MB for consumers and replication). Note that neither of these limits is an absolute maximum as explained in the next point.
Consumers and replicas can make progress if a message larger than the response/partition size limit is found. More concretely, if the first message in the first non-empty partition of the fetch is larger than either or both limits, the message will still be returned.
Overloaded constructors were added to kafka.api.FetchRequest and kafka.javaapi.FetchRequest to allow the caller to specify the order of the partitions (since order is significant in v3). The previously existing constructors were deprecated and the partitions are shuffled before the request is sent to avoid starvation issues.
New Protocol Versions ListOffsetRequest v1 supports accurate offset search based on timestamps.
MetadataResponse v2 introduces a new field: "cluster_id".
FetchRequest v3 supports limiting the response size (in addition to the existing per partition limit), it returns messages bigger than the limits if required to make progress and the order of partitions in the request is now significant.
JoinGroup v1 introduces a new field: "rebalance_timeout".
Upgrading from 0.8.x or 0.9.x to 0.10.0.00.10.0.0 has potential breaking changes (please review before upgrading) and possible performance impact following the upgrade. By following the recommended rolling upgrade plan below, you guarantee no downtime and no performance impact during and following the upgrade.Note: Because new protocols are introduced, it is important to upgrade your Kafka clusters before upgrading your clients. 041b061a72