Introduction
With an ever-expanding ocean of data, more and more organizations need to perform advanced and fast analytics over their business data, logs, and telemetry while seeking to reduce c...
I've followed the instructions and keep seeing below error in configuration. After updating to "json_mapping", the configuration file is accepted. The documentation needs an update.
[ERROR][logstash.outputs.kusto ] Missing a required setting for the kusto output plugin:
output {
kusto {
json_mapping => # SETTING MISSING
...
}
}
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Explorer","id":"message:1621539","revisionNum":83,"repliesCount":12,"author":{"__ref":"User:user:244679"},"depth":0,"hasGivenKudo":false,"board":{"__ref":"Blog:board:AzureDataExplorer"},"conversation":{"__ref":"Conversation:conversation:1621539"},"messagePolicies":{"__typename":"MessagePolicies","canPublishArticleOnEdit":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.forums.policy_can_publish_on_edit_workflow_action.accessDenied","key":"error.lithium.policies.forums.policy_can_publish_on_edit_workflow_action.accessDenied","args":[]}},"canModerateSpamMessage":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","key":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","args":[]}}},"contentWorkflow":{"__typename":"ContentWorkflow","state":"PUBLISH","scheduledPublishTime":null,"scheduledTimezone":null,"userContext":{"__typename":"MessageWorkflowContext","canSubmitForReview":null,"canEdit":false,"canRecall":null,"canSubmitForPublication":null,"canReturnToAuthor":null,"canPublish":null,"canReturnToReview":null,"canSchedule":false},"shortScheduledTimezone":null},"readOnly":false,"editFrozen":false,"moderationData":{"__ref":"ModerationData:moderation_data:1621539"},"teaser@stripHtml({\"truncateLength\":200})":" Migrating from Elasticsearch to Azure Data Explorer offers new opportunities. Read more about the aspects of migration from Elasticsearch to Azure Data Explorer \n ","body@stripHtml({\"truncateLength\":200})":" Introduction \n With an ever-expanding ocean of data, more and more organizations need to perform advanced and fast analytics over their business data, logs, and telemetry while seeking to reduce c...","body@stringLength":"144645","rawBody":"
Introduction
\n
With an ever-expandingocean of data,more and moreorganizations need to perform advanced and fast analytics over their business data, logs, and telemetrywhile seeking to reduce costs. Many of themare shifting towards Azure Data Explorer (ADX) and taking advantage of the significant benefitsitoffers to analyze billions of records quickly and cost-effectively. But sometimes they arealready using other tools.One common scenario is that organizations are already using Elasticsearch, Logstash, and Kibana(The ELK Stack). Migration between big data platforms sounds like a long and complicated process. But that’s not always true.Switching from ELK to Azure Data Explorer offers the opportunity to significantlyboost performance, reduce costs and improve the quality ofinsights by offering advanced query capabilities; all this without entering a long and complex migration, thanks to the tools described below. This blog post covers the following topics:
\n\n
Why organizations are moving to Azure Data Explorer
\n
How you can use Azure Data Explorer with Kibana
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What additional tools for data exploration, visualizations, and dashboards are available
\n
How you can send data to Azure Data Explorer throughLogstash (or other tools)
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How to use Logstash to migrate historical data from Elasticsearch to Azure Data Explorer
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Appendix: Step by step example – using Logstash to migrate historical data from Elasticsearch to Azure Data Explorer
\n\n
\n
1. Why organizationsare moving to Azure Data Explorer
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Azure Data Explorer is a highly scalable and fully managed dataanalytics service on the Microsoft Azure Cloud. ADX enables real-time analysis of large volumes of heterogeneous data in seconds and allows rapid iterations of data exploration to discover relevant insights. In short, the advantages of ADX can be summed up using the three Ps: Power, Performance, Price.
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Power
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Azure Data Explorer has a unique column-store inverted-index design. With the intuitive Kusto Query Language (KQL) you can query an enormous amount of dataandperform ad-hoc analytics in seconds.
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Users state that they find it easier to get more value and new insights from their data, at unprecedented speed and scale, using KQL. Their business troubleshooting became much faster, too. They are more engaged and understand the data better, since they can efficiently explore the data and run ad-hoc text parsing, create run-time calculated columns, aggregations, use joins, and plenty of other capabilities. These capabilities are natively supported without the need to modify the data. You don’t have to pre-organize the data, pre-define scripted fields, or de-normalize the data. There is no need to manage and take care of the hierarchy of objects like: Indices, Types, and IDs, as in other services.
\n
Azure Data Explorer’s machine-learning capabilities can identify patterns that are not obvious and detect differences in data sets. With capabilities like time series analysis, anomaly detection, and forecasting,you can uncover hidden insightsand easily point out issues or unusual relationships you may not even be aware of. You can also run inline Python and R as part of the queries. Also, Azure Data Explorer supports many communication APIs and client libraries, all of which make programmatic access easy.
\n
\n
Performance
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As a fully managed service, Azure Data Explorer gives you best-in-class performance. You can get results from one billion records in less than a second without modifying the data or metadata. ADXcan accept hundreds of billions of records a day and store this data reliably for months. ADX is specifically built tohandle complex analytics queries on the data and return answers in amatter of seconds, even when the data is a combination of structured, semi-structured (JSON-like nested types), and unstructured (free text).
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Price
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Azure Data Explorer is a managed Platform as a Service (PaaS), so you can reduce the management and operations overhead and save the cost of purchasing and maintaining hardware. Auto-scale capabilities are available for better cost control. Due to the efficient data ingestion process and querying engine, compute resources are consumed wisely and economically. The inverted-index columns-store design enables ADXto have a superb data compression ratio, which reduces the amount of required storage, compared to other services. You can run as many queries as you need, thanks to the optimized cost structure, without incurringadditional costs. In addition, ADX provides a granular cache policy that customers can set to differentiate betweenhot data cache and cold data cache, and get even more control over the costs.
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You can configure and estimate the costs with our cost estimator.
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2. How you can use Azure Data Explorer with Kibana
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As announced in aseparateblog post,we developed the K2Bridge (Kibana-Kusto Bridge), an open-sourceprojectthat enables you to connectyour familiarKibana’s Discover tab to Azure Data Explorer. Starting with Kibana 6.8, you can store your data in Azure Data Explorer on thebackend anduse K2Bridge to connect to Kibana. This way, your end-users can keep usingKibana’s Discover tabas their data exploration tool.Connect Kibana to Azure Data Explorer with K2Bridge
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3. What additional tools for data exploration, visualizations, and dashboards are available
Azure Data Explorer Web UI/Desktop application - to run queries, analyze and explore the datausing powerful KQL queries.
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The KQL render operator offers various out-of-the-box visualizations such as tables, pie charts, anomaly charts, and bar charts to depict query results. Query visualizations are helpful in anomaly detection, forecasting, machine-learning scenarios, and more. As described in the first chapter, you can efficiently run ad-hoc text parsing, create calculated columns, use joins and plenty of other capabilities, without any modifications or pre-organizations of the data.
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Azure Data Explorer dashboards- a web UI that enables you to run queries, build dashboards, and share themacross your organization.
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Integrations with Azure Monitor Workbooks - a flexible canvas for the creation of rich visual reports within the Azure portal.
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Integrations with other dashboard services like PowerBI and Grafana.
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4. How you can send data to Azure Data Explorer through Logstash (or other tools)
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Are you already using Logstash as the data pipeline? If so, redirecting the data to ADX is easy! You can use the open-source Logstash Output Plugin for Azure Data Explorer(a detailed example is described in the nextchapter), andkeep using the Logstash input plugin according to your specific source of the ongoing event stream, as you use it today.
\n
\n
There are many other ways to ingest data into Azure Data Explorer, including:
\n
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Ingestion using managed pipelines – using Azure Event Grid, Azure Data Factory (ADF), IoT Hub and Event Hub (Event Hub can receive data from several publishers, including Logstash and Filebeat, through Kafka).
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Ingestion using connectors and plugins - Logstash plugin, Kafka connector, Power Automate (Flow), Apache Spark connector
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Programmatic ingestion using SDKs
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Tools –LightIngest or One-click ingestion (a detailed example is described in the nextchapter)
5. How to use Logstash to migrate historical data from Elasticsearch to Azure Data Explorer
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Choose the data you care about
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When you decide to migrate historical data, it isa great opportunity to validate your data and needs. There is a good chance you can remove old, irrelevant, or unwanted data, and only move the data you care about. By migrating your freshest and latest data only, you can reduce costs and improve querying performance. Usually, when organizations migrate from Elasticsearch to Azure Data Explorer, they do notmigratehistorical data at all. The approach is a “side-by-side” migration: they “fork” their current data pipeline and ingest the ongoing live data to Azure Data Explorer (by using Logstash/Kafka/Event Hub connectors, for example) and after a while, they deactivate their Elasticsearch. Anyway, we show how you can migrate your historical data using Logstash.For efficiency, the Logstash output plugin section in the next tutorials contains a ‘query’ section in which you specify the data you care about and would like to export from Elasticsearch.
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\n
Data migration from Elasticsearch to Azure Data Explorer
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Here we describe two methods to migrate historical datafrom Elasticsearch using Logstash. See the appendix for a step-by-step tutorial.
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Method 1: Logstash and One-click Ingestion/LightIngest
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Use Logstash to export the data from Elasticsearch into CSV or JSON file(s), and then useAzure Data Explorer’sOne-Click Ingestionfeatureto ingest the data.
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This is an easy wayto quickly ramp-up and migrate data because One-Click Ingestion automatically generates the destination table and the ingestion mapping based on the structure of the data source (of course, you can edit the table schema if you want to).
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One-Click Ingestion supports ingesting up to 1 GB at a time. To ingest a more massiveamount of data, you can:\n
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Slice your data into multiple files and ingest them separately.
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UseLightIngest - a command-line utility for ad-hoc data ingestion. The utility can pull source data from a local folder (or from an Azure blob storage container).
\n
Use the second method described below.
\n
\n
\n
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Method 2: Using Logstash only (with the output plugin for Azure Data Explorer)
\n
\n
UseLogstash as a pipeline for both exporting data from Elasticsearch and ingesting itintoAzure Data Explorer.When you use this method,you should manually create theAzure Data Explorerdestination table and define the ingestion mapping.(You canautomatically generate thedestinationtableand thetable mappingby using One-Click Ingestion with sample data, as described in method 1first, and then usemethod 2forthe rest of the data)
\n
\n
Summary
\n
In this blog post, we talked about the advantages of Azure Data Explorer, went over several visualizations options, including theopen-source Kibana-Azure Data Explorer connector, andintroduced a variety of ways you can ingest your ongoing data into Azure Data Explorer.Then, wepresented twoways tomigratehistorical datafrom Elasticsearch to Azure Data Explorer. In the appendix, you can find two step-by-step sample scenarios for historical datamigration. Please do not hesitate to contact our team or leave a comment if you have any questions or concerns.
\n
\n
Appendix: Step-by-step example of historical data migration
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\n
Method 1: Logstash and One-Click Ingestion
\n\n
Use Logstash toexport the relevant datato migratefrom ElasticsearchintoaCSVor a JSONfile.Define a Logstash configuration file that uses the Elasticsearch input plugin to receive events from Elasticsearch. The output will be a CSV or a JSON file.\n\n
To export your data to aCSV file:use theCSV output plugin. For this example, the config file should look likethis: # Sample Logstash configuration: Elasticsearch -> CSV file\n\ninput {\n # Read documents from Elasticsearch matching the given query\n elasticsearch {\n hosts => [\"http://localhost:9200\"]\n index => \"storm_events\"\n query => '{ \"query\": { \"range\" : { \"StartTime\" : { \"gte\": \"2000-08-01 01:00:00.0000000\", \"lte\": \"now\" }}}}'\n }\n}\n\t\nfilter {\n ruby {\n init => \"\n begin\n @@csv_file = 'data-csv-export.csv'\n @@csv_headers = ['StartTime','EndTime','EpisodeId','EventId','State','EventType']\n if File.zero?(@@csv_file) || !File.exist?(@@csv_file)\n CSV.open(@@csv_file, 'w') do |csv|\n csv << @@csv_headers\n end\n end\n end\n \"\n code => \"\n begin\n event.get('@metadata')['csv_file'] = @@csv_file\n event.get('@metadata')['csv_headers'] = @@csv_headers\n end\n \"\n }\n}\n\noutput {\n csv {\n # elastic field name\n fields => [\"StartTime\",\"EndTime\",\"EpisodeId\",\"EventId\",\"State\",\"EventType\"]\n # This is path where we store output. \n path => \"./data-csv-export.csv\"\t\n }\n}\n
Thisconfig filespecifiesthatthe‘input’ for this process is theElasticsearch cluster, and the ‘output’ isthe CSV file.
\n
\n
Implementation note:The filter plugin adds a header with the field names to the CSV file's first line. This way,the destination table will beauto builtwiththesecolumn names.The pluginusesthe ‘init’optionofthe Ruby plugin toadd the header atLogstash startup-time.
\n
\n
\n
Alternatively, you can export your data to a JSON file,usingthe file outputformat. This is what our Logstash config file looks like: # Sample Logstash configuration: Elasticsearch -> JSON file\n\ninput {\n # Read documents from Elasticsearch matching the given query\n elasticsearch {\n hosts => [\"http://localhost:9200\"]\n index => \"storm_events\"\n query => '{ \"query\": { \"range\" : { \"StartTime\" : { \"gte\": \"2000-08-01 01:00:00.0000000\", \"lte\": \"now\" }}}}'\n }\n}\n\t\noutput {\n file {\n path => \"./output_file.json\"\n codec => json_lines\n }\n}\n
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The advantageof using JSON over CSVis that later, withOne-ClickIngestion,theAzure Data Explorer‘create table’and‘createjsonmapping’commandswill be auto-generated for you. Itwillsave youthe needtomanuallycreatetheJSON table mappingagain(in case you want to ingestyour ongoingdata with Logstashlater on.The Logstash output plugin usesjson mapping).
\n
\n
\n\n
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Start Logstash with the following command, from Logstash’s bin folder:logstash -f pipeline.conf
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If your pipeline is working correctly, you should see a series of events written to the console.
\n
The CSV/JSONfile should becreated at the destination you specified in the config file.
\n
Ingest your data into Azure Data Explorer withOne-ClickIngestion:\n\n
OpentheAzure Data Explorerweb UI. If this is the first time you are creating anAzure Data Explorercluster and database,seethis doc.
\n
Right-clickthedatabase name and selectIngest new Data.
\n
IntheIngest new datapage, use theCreate newoptiontoset the table name.
SelectEdit schema. You will be redirected to the schema of the table that will be created.
\n
Optionally,onthe schema page,click the column headers to change the data type or rename a column. You can also double-click the new column name to editit. For more information about this page, seethe doc.
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SelectStart Ingestionto ingest the dataintoAzure Data Explorer.
\n\n
\n
After a few minutes,depending on the size of the data set,your data will bestored inAzureData Explorer and ready for querying.
\n\n
Method 2: Using Logstash only
\n\n
Create an Azure Data Explorer cluster and database. Note: If you have already created your Azure Data Explorer cluster and database, you can skip this step. \n
More information on creating an Azure Data Explorer cluster and database can be found here.
\n
\n
\n
Create the destination table.
\n
Note: If you have already created your table with One-Click Ingestion, or in other ways, skip this step. Tip: The One-Click Ingestion tool auto-generates the table creation and the table mapping commands, based on the structure of sample JSON data you provide. If you use One-Click Ingestion with a JSON file, as described above, you can use the auto-generated commands, from the Editor section.
\nAuto-generate the table and its mapping using One-Click IngestionIn the Azure portal,underyour clusterpage,on the left menu,selectQuery(oruseAzure Data Explorer Web UI/Desktop application)and run thefollowing command. This commandcreatesa table with the name‘MyStormEvents’,withcolumns according to theschemaofthedata. .create tables MyStormEvents(StartTime:datetime,EndTime:datetime,EpisodeId:int,EventId:int,State:string,EventType:string)
\n
Create ingestion mapping. Note: If you used One-Click Ingestion with a JSON file, you can skip this step. This mapping is used at ingestion time to map incoming data to columns inside the ADX target table. The following command creates a new mapping, named ‘mymapping’, according to the data's schema. It extracts properties from the incoming temporary JSON on files, that will be automatically generated later, as noted by the path, and outputs them to the relevant column..create table MyStormEvents ingestion json mapping 'mymapping' '[{\"column\":\"StartTime\",\"path\":\"$.StartTime\"},{\"column\":\"EndTime\",\"path\":\"$.EndTime\"} ,{\"column\":\"EpisodeId\",\"path\":\"$.EpisodeId\"}, {\"column\":\"EventId\",\"path\":\"$.EventId\"}, {\"column\":\"State\",\"path\":\"$.State\"},{\"column\":\"EventType\",\"path\":\"$.EventType \"}]'
\n
Your table is ready to be ingestedwith datafrom your existing Elasticsearchindex.Toingestthehistoricaldatafrom Elasticsearch,you canuse the Elasticsearch input plugin to receive data from Elasticsearch,and theAzure Data Explorer(Kusto)outputpluginto ingest the data to ADX.\n\n
If youhave notused Logstash, you should first install it
\n
Install the Logstash output plugin forAzure Data Explorer,which sends the datato AzureData Explorer, by running:bin/logstash-plugin install logstash-output-kusto
\n
Definea Logstash configuration pipelinefilein your home Logstash directory. In the input plugin, you can specify a queryto filter your data according to a specific time range or anyother search criteria.This way, you can migrateonlythedata you care about. Inthisexample, the config file looksas follows (Replace all the placeholders with the relevant values for your setup. Credentials with ingest privileges are required to connect to ADX):input {\n# Read all documents from your Elasticsearch, from index “your_index_name”\n elasticsearch {\n hosts => [\"http://localhost:9200\"] \n\t\tindex => \" your_index_name \"\n query => '{ \"query\": { \"range\" : { \"StartTime\" : {\"gte\": \"2020-01-01 01:00:00.0000000\", \"lte\": \"now\"}} } }'\n }\n}\n\noutput {\n kusto {\n path => \"/tmp/kusto/%{+YYYY-MM-dd-HH-mm }.txt\"\n\t\tingest_url => \"https://<your cluster name>.<your cluster region>.kusto.windows.net” \n\t\tapp_id => \"<Your app id>\"\n\t\tapp_key => \"<Your app key>\"\n\t\tapp_tenant => \"<Your app tenant>\"\n database => \"<Your Azure Data Explorer DB name>\"\n\t\ttable => \"<Your table name>\" \n\t\tjson_mapping => \"<Yor mapping name>\"\n }\n}
\n
Edit yourconfiguration pipelinefileaccording to yourAzure Data Explorercluster detailsandstart Logstash with the following command, from Logstash’s bin folder:logstash -f pipeline.conf
\n
If your pipeline is working correctly, you should see a series of events written to the console.
\n
After a few minutes, run the followingAzureData Explorer query to see therecordsin the table you defined:MyStormEvents | count\n
The result is the number of records that were ingested into the table. It might take several minutes to ingest the entire dataset, depending on the size of the data set.The result of thisquery reflects the quantity of ingested records.Your data is now stored in Azure Data Explorer and is ready for querying!
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Migrating from Elasticsearch to Azure Data Explorer offers new opportunities. Read more about the aspects of migration from Elasticsearch to Azure Data Explorer
With an ever-expandingocean of data,more and moreorganizations need to perform advanced and fast analytics over their business data, logs, and telemetrywhile seeking to reduce costs. Many of themare shifting towards Azure Data Explorer (ADX) and taking advantage of the significant benefitsitoffers to analyze billions of records quickly and cost-effectively. But sometimes they arealready using other tools.One common scenario is that organizations are already using Elasticsearch, Logstash, and Kibana(The ELK Stack). Migration between big data platforms sounds like a long and complicated process. But that’s not always true.Switching from ELK to Azure Data Explorer offers the opportunity to significantlyboost performance, reduce costs and improve the quality ofinsights by offering advanced query capabilities; all this without entering a long and complex migration, thanks to the tools described below. This blog post covers the following topics:
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Why organizations are moving to Azure Data Explorer
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How you can use Azure Data Explorer with Kibana
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What additional tools for data exploration, visualizations, and dashboards are available
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How you can send data to Azure Data Explorer throughLogstash (or other tools)
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How to use Logstash to migrate historical data from Elasticsearch to Azure Data Explorer
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Appendix: Step by step example – using Logstash to migrate historical data from Elasticsearch to Azure Data Explorer
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1. Why organizationsare moving to Azure Data Explorer
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Azure Data Explorer is a highly scalable and fully managed dataanalytics service on the Microsoft Azure Cloud. ADX enables real-time analysis of large volumes of heterogeneous data in seconds and allows rapid iterations of data exploration to discover relevant insights. In short, the advantages of ADX can be summed up using the three Ps: Power, Performance, Price.
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Power
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Azure Data Explorer has a unique column-store inverted-index design. With the intuitive Kusto Query Language (KQL) you can query an enormous amount of dataandperform ad-hoc analytics in seconds.
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Users state that they find it easier to get more value and new insights from their data, at unprecedented speed and scale, using KQL. Their business troubleshooting became much faster, too. They are more engaged and understand the data better, since they can efficiently explore the data and run ad-hoc text parsing, create run-time calculated columns, aggregations, use joins, and plenty of other capabilities. These capabilities are natively supported without the need to modify the data. You don’t have to pre-organize the data, pre-define scripted fields, or de-normalize the data. There is no need to manage and take care of the hierarchy of objects like: Indices, Types, and IDs, as in other services.
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Azure Data Explorer’s machine-learning capabilities can identify patterns that are not obvious and detect differences in data sets. With capabilities like time series analysis, anomaly detection, and forecasting,you can uncover hidden insightsand easily point out issues or unusual relationships you may not even be aware of. You can also run inline Python and R as part of the queries. Also, Azure Data Explorer supports many communication APIs and client libraries, all of which make programmatic access easy.
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Performance
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As a fully managed service, Azure Data Explorer gives you best-in-class performance. You can get results from one billion records in less than a second without modifying the data or metadata. ADXcan accept hundreds of billions of records a day and store this data reliably for months. ADX is specifically built tohandle complex analytics queries on the data and return answers in amatter of seconds, even when the data is a combination of structured, semi-structured (JSON-like nested types), and unstructured (free text).
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Price
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Azure Data Explorer is a managed Platform as a Service (PaaS), so you can reduce the management and operations overhead and save the cost of purchasing and maintaining hardware. Auto-scale capabilities are available for better cost control. Due to the efficient data ingestion process and querying engine, compute resources are consumed wisely and economically. The inverted-index columns-store design enables ADXto have a superb data compression ratio, which reduces the amount of required storage, compared to other services. You can run as many queries as you need, thanks to the optimized cost structure, without incurringadditional costs. In addition, ADX provides a granular cache policy that customers can set to differentiate betweenhot data cache and cold data cache, and get even more control over the costs.
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You can configure and estimate the costs with our cost estimator.
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2. How you can use Azure Data Explorer with Kibana
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As announced in aseparateblog post,we developed the K2Bridge (Kibana-Kusto Bridge), an open-sourceprojectthat enables you to connectyour familiarKibana’s Discover tab to Azure Data Explorer. Starting with Kibana 6.8, you can store your data in Azure Data Explorer on thebackend anduse K2Bridge to connect to Kibana. This way, your end-users can keep usingKibana’s Discover tabas their data exploration tool.Connect Kibana to Azure Data Explorer with K2Bridge
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3. What additional tools for data exploration, visualizations, and dashboards are available
Azure Data Explorer Web UI/Desktop application - to run queries, analyze and explore the datausing powerful KQL queries.
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The KQL render operator offers various out-of-the-box visualizations such as tables, pie charts, anomaly charts, and bar charts to depict query results. Query visualizations are helpful in anomaly detection, forecasting, machine-learning scenarios, and more. As described in the first chapter, you can efficiently run ad-hoc text parsing, create calculated columns, use joins and plenty of other capabilities, without any modifications or pre-organizations of the data.
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Azure Data Explorer dashboards- a web UI that enables you to run queries, build dashboards, and share themacross your organization.
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Integrations with Azure Monitor Workbooks - a flexible canvas for the creation of rich visual reports within the Azure portal.
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Integrations with other dashboard services like PowerBI and Grafana.
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4. How you can send data to Azure Data Explorer through Logstash (or other tools)
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Are you already using Logstash as the data pipeline? If so, redirecting the data to ADX is easy! You can use the open-source Logstash Output Plugin for Azure Data Explorer(a detailed example is described in the nextchapter), andkeep using the Logstash input plugin according to your specific source of the ongoing event stream, as you use it today.
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There are many other ways to ingest data into Azure Data Explorer, including:
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Ingestion using managed pipelines – using Azure Event Grid, Azure Data Factory (ADF), IoT Hub and Event Hub (Event Hub can receive data from several publishers, including Logstash and Filebeat, through Kafka).
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Ingestion using connectors and plugins - Logstash plugin, Kafka connector, Power Automate (Flow), Apache Spark connector
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Programmatic ingestion using SDKs
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Tools –LightIngest or One-click ingestion (a detailed example is described in the nextchapter)
5. How to use Logstash to migrate historical data from Elasticsearch to Azure Data Explorer
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Choose the data you care about
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When you decide to migrate historical data, it isa great opportunity to validate your data and needs. There is a good chance you can remove old, irrelevant, or unwanted data, and only move the data you care about. By migrating your freshest and latest data only, you can reduce costs and improve querying performance. Usually, when organizations migrate from Elasticsearch to Azure Data Explorer, they do notmigratehistorical data at all. The approach is a “side-by-side” migration: they “fork” their current data pipeline and ingest the ongoing live data to Azure Data Explorer (by using Logstash/Kafka/Event Hub connectors, for example) and after a while, they deactivate their Elasticsearch. Anyway, we show how you can migrate your historical data using Logstash.For efficiency, the Logstash output plugin section in the next tutorials contains a ‘query’ section in which you specify the data you care about and would like to export from Elasticsearch.
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Data migration from Elasticsearch to Azure Data Explorer
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Here we describe two methods to migrate historical datafrom Elasticsearch using Logstash. See the appendix for a step-by-step tutorial.
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Method 1: Logstash and One-click Ingestion/LightIngest
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Use Logstash to export the data from Elasticsearch into CSV or JSON file(s), and then useAzure Data Explorer’sOne-Click Ingestionfeatureto ingest the data.
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This is an easy wayto quickly ramp-up and migrate data because One-Click Ingestion automatically generates the destination table and the ingestion mapping based on the structure of the data source (of course, you can edit the table schema if you want to).
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One-Click Ingestion supports ingesting up to 1 GB at a time. To ingest a more massiveamount of data, you can:\n
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Slice your data into multiple files and ingest them separately.
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UseLightIngest - a command-line utility for ad-hoc data ingestion. The utility can pull source data from a local folder (or from an Azure blob storage container).
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Use the second method described below.
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Method 2: Using Logstash only (with the output plugin for Azure Data Explorer)
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UseLogstash as a pipeline for both exporting data from Elasticsearch and ingesting itintoAzure Data Explorer.When you use this method,you should manually create theAzure Data Explorerdestination table and define the ingestion mapping.(You canautomatically generate thedestinationtableand thetable mappingby using One-Click Ingestion with sample data, as described in method 1first, and then usemethod 2forthe rest of the data)
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Summary
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In this blog post, we talked about the advantages of Azure Data Explorer, went over several visualizations options, including theopen-source Kibana-Azure Data Explorer connector, andintroduced a variety of ways you can ingest your ongoing data into Azure Data Explorer.Then, wepresented twoways tomigratehistorical datafrom Elasticsearch to Azure Data Explorer. In the appendix, you can find two step-by-step sample scenarios for historical datamigration. Please do not hesitate to contact our team or leave a comment if you have any questions or concerns.
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Appendix: Step-by-step example of historical data migration
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Method 1: Logstash and One-Click Ingestion
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Use Logstash toexport the relevant datato migratefrom ElasticsearchintoaCSVor a JSONfile.Define a Logstash configuration file that uses the Elasticsearch input plugin to receive events from Elasticsearch. The output will be a CSV or a JSON file.\n\n
To export your data to aCSV file:use theCSV output plugin. For this example, the config file should look likethis:
# Sample Logstash configuration: Elasticsearch -> CSV file\n\ninput {\n # Read documents from Elasticsearch matching the given query\n elasticsearch {\n hosts => [\"http://localhost:9200\"]\n index => \"storm_events\"\n query => '{ \"query\": { \"range\" : { \"StartTime\" : { \"gte\": \"2000-08-01 01:00:00.0000000\", \"lte\": \"now\" }}}}'\n }\n}\n\t\nfilter {\n ruby {\n init => \"\n begin\n @@csv_file = 'data-csv-export.csv'\n @@csv_headers = ['StartTime','EndTime','EpisodeId','EventId','State','EventType']\n if File.zero?(@@csv_file) || !File.exist?(@@csv_file)\n CSV.open(@@csv_file, 'w') do |csv|\n csv << @@csv_headers\n end\n end\n end\n \"\n code => \"\n begin\n event.get('@metadata')['csv_file'] = @@csv_file\n event.get('@metadata')['csv_headers'] = @@csv_headers\n end\n \"\n }\n}\n\noutput {\n csv {\n # elastic field name\n fields => [\"StartTime\",\"EndTime\",\"EpisodeId\",\"EventId\",\"State\",\"EventType\"]\n # This is path where we store output. \n path => \"./data-csv-export.csv\"\t\n }\n}
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Thisconfig filespecifiesthatthe‘input’ for this process is theElasticsearch cluster, and the ‘output’ isthe CSV file.
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Implementation note:The filter plugin adds a header with the field names to the CSV file's first line. This way,the destination table will beauto builtwiththesecolumn names.The pluginusesthe ‘init’optionofthe Ruby plugin toadd the header atLogstash startup-time.
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Alternatively, you can export your data to a JSON file,usingthe file outputformat. This is what our Logstash config file looks like:
The advantageof using JSON over CSVis that later, withOne-ClickIngestion,theAzure Data Explorer‘create table’and‘createjsonmapping’commandswill be auto-generated for you. Itwillsave youthe needtomanuallycreatetheJSON table mappingagain(in case you want to ingestyour ongoingdata with Logstashlater on.The Logstash output plugin usesjson mapping).
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Start Logstash with the following command, from Logstash’s bin folder:
logstash -f pipeline.conf
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If your pipeline is working correctly, you should see a series of events written to the console.
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The CSV/JSONfile should becreated at the destination you specified in the config file.
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Ingest your data into Azure Data Explorer withOne-ClickIngestion:\n\n
OpentheAzure Data Explorerweb UI. If this is the first time you are creating anAzure Data Explorercluster and database,seethis doc.
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Right-clickthedatabase name and selectIngest new Data.
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IntheIngest new datapage, use theCreate newoptiontoset the table name.
SelectEdit schema. You will be redirected to the schema of the table that will be created.
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Optionally,onthe schema page,click the column headers to change the data type or rename a column. You can also double-click the new column name to editit. For more information about this page, seethe doc.
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SelectStart Ingestionto ingest the dataintoAzure Data Explorer.
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After a few minutes,depending on the size of the data set,your data will bestored inAzureData Explorer and ready for querying.
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Method 2: Using Logstash only
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Create an Azure Data Explorer cluster and database. Note: If you have already created your Azure Data Explorer cluster and database, you can skip this step. \n
More information on creating an Azure Data Explorer cluster and database can be found here.
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Create the destination table.
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Note: If you have already created your table with One-Click Ingestion, or in other ways, skip this step. Tip: The One-Click Ingestion tool auto-generates the table creation and the table mapping commands, based on the structure of sample JSON data you provide. If you use One-Click Ingestion with a JSON file, as described above, you can use the auto-generated commands, from the Editor section.
\nAuto-generate the table and its mapping using One-Click IngestionIn the Azure portal,underyour clusterpage,on the left menu,selectQuery(oruseAzure Data Explorer Web UI/Desktop application)and run thefollowing command. This commandcreatesa table with the name‘MyStormEvents’,withcolumns according to theschemaofthedata.
Create ingestion mapping. Note: If you used One-Click Ingestion with a JSON file, you can skip this step. This mapping is used at ingestion time to map incoming data to columns inside the ADX target table. The following command creates a new mapping, named ‘mymapping’, according to the data's schema. It extracts properties from the incoming temporary JSON on files, that will be automatically generated later, as noted by the path, and outputs them to the relevant column.
Your table is ready to be ingestedwith datafrom your existing Elasticsearchindex.Toingestthehistoricaldatafrom Elasticsearch,you canuse the Elasticsearch input plugin to receive data from Elasticsearch,and theAzure Data Explorer(Kusto)outputpluginto ingest the data to ADX.\n\n
If youhave notused Logstash, you should first install it
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Install the Logstash output plugin forAzure Data Explorer,which sends the datato AzureData Explorer, by running:
bin/logstash-plugin install logstash-output-kusto
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Definea Logstash configuration pipelinefilein your home Logstash directory. In the input plugin, you can specify a queryto filter your data according to a specific time range or anyother search criteria.This way, you can migrateonlythedata you care about. Inthisexample, the config file looksas follows (Replace all the placeholders with the relevant values for your setup. Credentials with ingest privileges are required to connect to ADX):
Edit yourconfiguration pipelinefileaccording to yourAzure Data Explorercluster detailsandstart Logstash with the following command, from Logstash’s bin folder:
logstash -f pipeline.conf
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If your pipeline is working correctly, you should see a series of events written to the console.
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After a few minutes, run the followingAzureData Explorer query to see therecordsin the table you defined:
MyStormEvents | count
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The result is the number of records that were ingested into the table. It might take several minutes to ingest the entire dataset, depending on the size of the data set.The result of thisquery reflects the quantity of ingested records.Your data is now stored in Azure Data Explorer and is ready for querying!
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I've followed the instructions and keep seeing below error in configuration. After updating to \"json_mapping\", the configuration file is accepted. The documentation needs an update.
[ERROR][logstash.outputs.kusto ] Missing a required setting for the kusto output plugin:\n\noutput {\nkusto {\njson_mapping => # SETTING MISSING\n...\n}\n}
","body@stringLength":"434","rawBody":"
I've followed the instructions and keep seeing below error in configuration. After updating to \"json_mapping\", the configuration file is accepted. The documentation needs an update.
[ERROR][logstash.outputs.kusto ] Missing a required setting for the kusto output plugin:\n\noutput {\nkusto {\njson_mapping => # SETTING MISSING\n...\n}\n}
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