The Data Collector Set Or One Of Its Dependencies Is Already In Use [CRACKED]
On an internet search I found multiple things, restart the server, look for running data collectors, I tried everything I found as a possible fix with no luck, until I noticed a pattern, all DCs where I couldn't run the said Data Collector were running PaloAlto Cortex, I also found some Data Collectors that seemed to belong to the product in Startup Event Trace Sessions under System Data Collector sets in Performance Monitor, and in particular, XdrAgentLog was enabled.
The Data Collector Set or one of its dependencies is already in use
I asked the customer to disable the Cortex agent and I could succesfully run the Active Directory Data Collector, we later re-enabled it and will probably open a support ticket to find a way not to do that while we need to run the data collector.
Before you can use PerfMon, you set up a data collector set, which is how PerfMon stores the data that it collects. To collect information about Tableau Server processes with PerfMon, Tableau Server must be running when you create the data collector set. The data that you collect in PerfMon are often referred to as performance counters.
In the left pane, right-click the name of the data collector set that you created and click Start. The Windows Performance Monitor tool starts monitoring your server and storing information in the location that you specified.
As you already may know to make a user defined alert start automatically after server restart you can navigate to the Task Scheduler\Task Scheduler Library\Microsoft\Windows\PLA and configure the corresponding scheduled task to start the alert at system startup (each data collector set has a corresponding scheduled task of the same name in the PLA section of the Task Scheduler)
If the scheduled tasks (and the data collector sets they start, of course) to be created on each server are the same across at least some of your servers, you can automate the process of creating the scheduled tasks on those server by using domain GPOs.
WMI also provides instrumentation for Windows-based applications. Application providers can use this to expose details about the performance and health of their applications. Most Microsoft tools, applications, and server software already use WMI to provide performance data and additional metrics.
For ingesting data from sources like Kafka and Kinesis that are not present in the SparkStreaming core API, you will have to add the correspondingartifact spark-streaming-xyz_2.12 to the dependencies. For example,some of the common ones are as follows.
We have already taken a look at the ssc.socketTextStream(...) in the quick examplewhich creates a DStream from textdata received over a TCP socket connection. Besides sockets, the StreamingContext API providesmethods for creating DStreams from files as input sources.
Configuring write-ahead logs - Since Spark 1.2,we have introduced write-ahead logs for achieving strongfault-tolerance guarantees. If enabled, all the data received from a receiver gets written intoa write-ahead log in the configuration checkpoint directory. This prevents data loss on driverrecovery, thus ensuring zero data loss (discussed in detail in theFault-tolerance Semantics section). This can be enabled by settingthe configuration parameterspark.streaming.receiver.writeAheadLog.enable to true. However, these stronger semantics maycome at the cost of the receiving throughput of individual receivers. This can be corrected byrunning more receivers in parallelto increase aggregate throughput. Additionally, it is recommended that the replication of thereceived data within Spark be disabled when the write-ahead log is enabled as the log is alreadystored in a replicated storage system. This can be done by setting the storage level for theinput stream to StorageLevel.MEMORY_AND_DISK_SER. While using S3 (or any file system thatdoes not support flushing) for write-ahead logs, please remember to enablespark.streaming.driver.writeAheadLog.closeFileAfterWrite andspark.streaming.receiver.writeAheadLog.closeFileAfterWrite. SeeSpark Streaming Configuration for more details.Note that Spark will not encrypt data written to the write-ahead log when I/O encryption isenabled. If encryption of the write-ahead log data is desired, it should be stored in a filesystem that supports encryption natively.
If all of the input data is already present in a fault-tolerant file system likeHDFS, Spark Streaming can always recover from any failure and process all of the data. This givesexactly-once semantics, meaning all of the data will be processed exactly once no matter what fails.
Update external system with this blob transactionally (that is, exactly once, atomically) using the identifier. That is, if the identifier is not already committed, commit the partition data and the identifier atomically. Else, if this was already committed, skip the update.
You can monitor your own data using custom metrics, CloudWatch Logs, or both. You may want to use custom metrics if your data is not already produced in log format, for example operating system processes or performance measurements. Or, you may want to write your own application or script, or one provided by an AWS partner. If you want to store and save individual measurements along with additional detail, you may want to use CloudWatch Logs.
CloudWatch Logs lets you test the Metric Filter patterns you want before you create a Metric Filter. You can test your patterns against your own log data that is already in CloudWatch Logs or you can supply your own log events to test. Testing your pattern will show you which log events matched the Metric Filter pattern and, if extracting values, what the extracted value is in the test data. Metric Filter testing is available for use in the console and the CLI.
In this post in the microservices series we will study how to manage inter-service dependencies and how to deal with implicit interfaces in the form of data sharing. We will also extend our existing examples from the microservices series to show some of the concepts in this post. If you haven't done so, check An Introduction to Microservices, Part 3.
In a traditional monolithic application, dependencies usually appear as method calls. It is usually a matter of importing the right parts of the project to access their functionality. In esence, doing so creates a dependency between the different parts of the application. With microservices, each microservice is meant to operate on its own. However, sometimes one may find that to provide certain functionality, access to some other part of the system is necessary. In concrete, some part of the system needs access to data managed by other part of the system.
An important part of managing dependencies has to do with what happens when a service is updated to fit new requirements or solve a design issue. Other microservices may depend on the semantics of the old version or worse: depend on the way data is modeled in the database. As microservices are developed in isolation, this means a team usually cannot wait for another team to make the necessary changes to a dependent service before going live. The way to solve this is through versioning. All microservices should make it clear what version of a different microservice they require and what version they are. A good way of versioning is through semantic versioning, that is, keeping versions as a set of numbers that make it clear when a breaking change happens (for instance, one number can mean that the API has been modified).
The problem of dependency and changes (versions) rises an interesting question: what if things break when a dependency is modified (in spite of our efforts to use versioning)? Failure. We have discussed this briefly in previous posts in this series and now is good time to remember it: graceful failure is key in a distributed architecture. Things will fail. Services should do whatever is possible to run even when dependencies fail. It is perfectly acceptable to have a fallback service, a local cache or even to return less data than requested. Crashes should be avoided, and all dependencies should be treated as things prone to failure.
The Asset Cache is where Unity stores the imported versions of assets. Because Unity can always recreate these imported versions from the source asset file and its dependencies, these imported versions are treated as a cache of pre-calculated data, which saves time when you use Unity. For this reason, you should exclude the files in the Asset Cache from version controlA system for managing file changes. You can use Unity in conjunction with most common version control tools, including Perforce, Git, Mercurial and PlasticSCM. More infoSee in Glossary systems.
The most common symptoms seen when troubleshooting Perfmon instability are issues where Perfmon is having difficulty initiating a connection to a remote host. This must succeed between the collector and host in order for the LogicMonitorActive Discovery mechanism to detect which Perfmon performance counters are available on the remote host and to read data from the host.
The Native Image builder or native-image is a utility that processes all classes of an application and their dependencies, including those from the JDK.It statically analyzes these data to determine which classes and methods are reachable during the application execution.Then it ahead-of-time compiles that reachable code and data to a native executable for a specific operating system and architecture.This entire process is called building an image (or the image build time) to clearly distinguish it from the compilation of Java source code to bytecode.
The goal of the exporters is to take the telemetry data as it is represented in the collector (OTEL data), convert it to a different format when needed (like Jaeger), and then send it to the endpoint you define. The sending part is done using the OTLP format, over either HTTP or gRPC.