T Racks 24 V 2.0.1 Authorization Code
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.
T Racks 24 V 2.0.1 Authorization Code
The high-level consumer tracks the maximum offset it has consumed in each partition and periodically commits its offset vector so that it can resume from those offsets in the event of a restart. Kafka provides the option to store all the offsets for a given consumer group in a designated broker (for that group) called the offset manager. i.e., any consumer instance in that consumer group should send its offset commits and fetches to that offset manager (broker). The high-level consumer handles this automatically. If you use the simple consumer you will need to manage offsets manually. This is currently unsupported in the Java simple consumer which can only commit or fetch offsets in ZooKeeper. If you use the Scala simple consumer you can discover the offset manager and explicitly commit or fetch offsets to the offset manager. A consumer can look up its offset manager by issuing a GroupCoordinatorRequest to any Kafka broker and reading the GroupCoordinatorResponse which will contain the offset manager. The consumer can then proceed to commit or fetch offsets from the offsets manager broker. In case the offset manager moves, the consumer will need to rediscover the offset manager. If you wish to manage your offsets manually, you can take a look at these code samples that explain how to issue OffsetCommitRequest and OffsetFetchRequest.
Shower is a C-interface to EGS4, a Monte Carlo electromagnetic shower simulation program. EGS4 (developped at SLAC) is a set of subroutines that generates and tracks particle in a electromagnetic shower. In the conventional EGS4 code system, the user must supply their own input and output routines and problem geometry definition written in the MORTRAN language. This arduous process has been replaced by the C-code interface provided here by reading and writing input and output particle information as data files, the geometry definition as a file of namelist type commands, and other information in an addition summary data file. All data files are in SDDS format, and therefore compatible with other processing and tracking programs.
After IFTTT has received an authorization code for the user, it will make a POST request to your OAuth2 Token URL, specified in the Service Authentication settings, and exchange the code for an access token.
For authentication purposes the username is matched against data stored in the username column, the password is expected to be stored as a hex-encoded MD5 hash in the password column, and the user role for authorization purposes is stored in the role column. 350c69d7ab