Tnl Configuration File Download UPDATED

Prometheus is configured via command-line flags and a configuration file. Whilethe command-line flags configure immutable system parameters (such as storagelocations, amount of data to keep on disk and in memory, etc.), theconfiguration file defines everything related to scraping jobs and theirinstances, as well aswhich rule files to load.

Prometheus can reload its configuration at runtime. If the new configurationis not well-formed, the changes will not be applied.A configuration reload is triggered by sending a SIGHUP to the Prometheus process orsending a HTTP POST request to the /-/reload endpoint (when the --web.enable-lifecycle flag is enabled).This will also reload any configured rule files.

Tnl Configuration File Download

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A scrape_config section specifies a set of targets and parameters describing howto scrape them. In the general case, one scrape configuration specifies a singlejob. In advanced configurations, this may change.

DigitalOcean SD configurations allow retrieving scrape targets from DigitalOcean'sDroplets API.This service discovery uses the public IPv4 address by default, by that can bechanged with relabeling, as demonstrated in the Prometheus digitalocean-sdconfiguration file.

The services role discovers all Swarm servicesand exposes their ports as targets. For each published port of a service, asingle target is generated. If a service has no published ports, a target perservice is created using the port parameter defined in the SD configuration.

The tasks role discovers all Swarm tasksand exposes their ports as targets. For each published port of a task, a singletarget is generated. If a task has no published ports, a target per task iscreated using the port parameter defined in the SD configuration.

A DNS-based service discovery configuration allows specifying a set of DNSdomain names which are periodically queried to discover a list of targets. TheDNS servers to be contacted are read from /etc/resolv.conf.

OVHcloud SD configurations allow retrieving scrape targets from OVHcloud's dedicated servers and VPS usingtheir API.Prometheus will periodically check the REST endpoint and create a target for every discovered server.The role will try to use the public IPv4 address as default address, if there's none it will try to use the IPv6 one. This may be changed with relabeling.For OVHcloud's public cloud instances you can use the openstacksdconfig.

Hetzner SD configurations allow retrieving scrape targets fromHetzner Cloud API andRobot API.This service discovery uses the public IPv4 address by default, but that can bechanged with relabeling, as demonstrated in the Prometheus hetzner-sdconfiguration file.

IONOS SD configurations allows retrieving scrape targets fromIONOS Cloud API. This service discovery uses thefirst NICs IP address by default, but that can be changed with relabeling. Thefollowing meta labels are available on all targets duringrelabeling:

Linode SD configurations allow retrieving scrape targets from Linode'sLinode APIv4.This service discovery uses the public IPv4 address by default, by that can bechanged with relabeling, as demonstrated in the Prometheus linode-sdconfiguration file.

Marathon SD configurations allow retrieving scrape targets using theMarathon REST API. Prometheuswill periodically check the REST endpoint for currently running tasks andcreate a target group for every app that has at least one healthy task.

By default every app listed in Marathon will be scraped by Prometheus. If not allof your services provide Prometheus metrics, you can use a Marathon label andPrometheus relabeling to control which instances will actually be scraped.See the Prometheus marathon-sd configuration filefor a practical example on how to set up your Marathon app and your Prometheusconfiguration.

Relabeling is a powerful tool to dynamically rewrite the label set of a target beforeit gets scraped. Multiple relabeling steps can be configured per scrape configuration.They are applied to the label set of each target in order of their appearancein the configuration file.

Initially, aside from the configured per-target labels, a target's joblabel is set to the job_name value of the respective scrape configuration.The __address__ label is set to the : address of the target.After relabeling, the instance label is set to the value of __address__ by default ifit was not set during relabeling. The __scheme__ and __metrics_path__ labelsare set to the scheme and metrics path of the target respectively. The __param_label is set to the value of the first passed URL parameter called .

Metric relabeling is applied to samples as the last step before ingestion. Ithas the same configuration format and actions as target relabeling. Metricrelabeling does not apply to automatically generated timeseries such as up.

The term is very common in computer science and mathematics, and in scientific and technological fields in general. Thus, for example, two scientists won a 1962 Nobel Prize for their description of the configuration of the DNA molecule. Since then, researchers have studied what different configurations within the DNA strands mean and what they control, and genetic engineers have tried to configure or reconfigure DNA in new ways to prevent or treat diseases.

1) In computers and computer networks, a configuration often refers to the specific hardware and software details in terms of devices attached, capacity or capability, and exactly what the system is made up of.

While numbers without units are generally interpreted as bytes, a few are interpreted as KiB or MiB.See documentation of individual configuration properties. Specifying units is desirable wherepossible.

The Spark shell and spark-submittool support two ways to load configurations dynamically. The first is command line options,such as --master, as shown above. spark-submit can accept any Spark property using the --conf/-cflag, but uses special flags for properties that play a part in launching the Spark application.Running ./bin/spark-submit --help will show the entire list of these options.

Any values specified as flags or in the properties file will be passed on to the applicationand merged with those specified through SparkConf. Properties set directly on the SparkConftake highest precedence, then flags passed to spark-submit or spark-shell, then optionsin the spark-defaults.conf file. A few configuration keys have been renamed since earlierversions of Spark; in such cases, the older key names are still accepted, but take lowerprecedence than any instance of the newer key.

Server configurations are set in Spark Connect server, for example, when you start the Spark Connect server with ./sbin/start-connect-server.sh.They are typically set via the config file and command-lineoptions with --conf/-c.

The initial number of shuffle partitions before coalescing. If not set, it equals to spark.sql.shuffle.partitions. This configuration only has an effect when 'spark.sql.adaptive.enabled' and 'spark.sql.adaptive.coalescePartitions.enabled' are both true.

The ratio of the number of two buckets being coalesced should be less than or equal to this value for bucket coalescing to be applied. This configuration only has an effect when 'spark.sql.bucketing.coalesceBucketsInJoin.enabled' is set to true.

If the configuration property is set to true, java.time.Instant and java.time.LocalDate classes of Java 8 API are used as external types for Catalyst's TimestampType and DateType. If it is set to false, java.sql.Timestamp and java.sql.Date are used for the same purpose.

When PRETTY, the error message consists of textual representation of error class, message and query context. The MINIMAL and STANDARD formats are pretty JSON formats where STANDARD includes an additional JSON field message. This configuration property influences on error messages of Thrift Server and SQL CLI while running queries.

Whether to ignore corrupt files. If true, the Spark jobs will continue to run when encountering corrupted files and the contents that have been read will still be returned. This configuration is effective only when using file-based sources such as Parquet, JSON and ORC.

Whether to ignore missing files. If true, the Spark jobs will continue to run when encountering missing files and the contents that have been read will still be returned. This configuration is effective only when using file-based sources such as Parquet, JSON and ORC.

The suggested (not guaranteed) maximum number of split file partitions. If it is set, Spark will rescale each partition to make the number of partitions is close to this value if the initial number of partitions exceeds this value. This configuration is effective only when using file-based sources such as Parquet, JSON and ORC.

The suggested (not guaranteed) minimum number of split file partitions. If not set, the default value is spark.sql.leafNodeDefaultParallelism. This configuration is effective only when using file-based sources such as Parquet, JSON and ORC.

When true, also tries to merge possibly different but compatible Parquet schemas in different Parquet data files. This configuration is only effective when "spark.sql.hive.convertMetastoreParquet" is true.

When true, check all the partition paths under the table's root directory when reading data stored in HDFS. This configuration will be deprecated in the future releases and replaced by spark.files.ignoreMissingFiles.

Configures a list of rules to be disabled in the optimizer, in which the rules are specified by their rule names and separated by comma. It is not guaranteed that all the rules in this configuration will eventually be excluded, as some rules are necessary for correctness. The optimizer will log the rules that have indeed been excluded.

When enabled, Parquet timestamp columns with annotation isAdjustedToUTC = false are inferred as TIMESTAMP_NTZ type during schema inference. Otherwise, all the Parquet timestamp columns are inferred as TIMESTAMP_LTZ types. Note that Spark writes the output schema into Parquet's footer metadata on file writing and leverages it on file reading. Thus this configuration only affects the schema inference on Parquet files which are not written by Spark. ff782bc1db