MicrosoftI'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
...
}
}
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 costs. Many of them are shifting towards Azure Data Explorer (ADX) and taking advantage of the significant benefits it offers to analyze billions of records quickly and cost-effectively.
But sometimes they are already 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 significantly boost performance, reduce costs and improve the quality of insights 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:
Azure Data Explorer is a highly scalable and fully managed data analytics 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.
\n
Power
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.
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 insights and 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
Performance
\n
Price
\nYou can configure and estimate the costs with our cost estimator.
\n\n
As announced in a separate blog post, we developed the K2Bridge (Kibana-Kusto Bridge), an open-source project that enables you to connect your familiar Kibana’s Discover tab to Azure Data Explorer. Starting with Kibana 6.8, you can store your data in Azure Data Explorer on the back end and use K2Bridge to connect to Kibana. This way, your end-users can keep using Kibana’s Discover tab as their data exploration tool.
\n
Azure Data Explorer offers various other exploration and visualization capabilities that take advantage of the rich and built-in analyses options of KQL, including:
\nAre 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 next chapter), and keep using the Logstash input plugin according to your specific source of the ongoing event stream, as you use it today.
\n
There are many other ways to ingest data into Azure Data Explorer, including:
For more information, please refer to the data ingestion overview.
\n\n
When you decide to migrate historical data, it is a 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 not migrate historical 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.
\n
Here we describe two methods to migrate historical data from Elasticsearch using Logstash. See the appendix for a step-by-step tutorial.
\n
Method 1: Logstash and One-click Ingestion/LightIngest
\nUse Logstash to export the data from Elasticsearch into CSV or JSON file(s), and then use Azure Data Explorer’s One-Click Ingestion feature to ingest the data.
\nMethod 2: Using Logstash only (with the output plugin for Azure Data Explorer)
\nUse Logstash as a pipeline for both exporting data from Elasticsearch and ingesting it into Azure Data Explorer. When you use this method, you should manually create the Azure Data Explorer destination table and define the ingestion mapping. (You can automatically generate the destination table and the table mapping by using One-Click Ingestion with sample data, as described in method 1 first, and then use method 2 for the rest of the data)
\nIn this blog post, we talked about the advantages of Azure Data Explorer, went over several visualizations options, including the open-source Kibana-Azure Data Explorer connector, and introduced a variety of ways you can ingest your ongoing data into Azure Data Explorer. Then, we presented two ways to migrate historical data from Elasticsearch to Azure Data Explorer.
In the appendix, you can find two step-by-step sample scenarios for historical data migration.
Please do not hesitate to contact our team or leave a comment if you have any questions or concerns.
This config file specifies that the ‘input’ for this process is the Elasticsearch cluster, and the ‘output’ is the CSV file.
\nMore information on creating an Azure Data Explorer cluster and database can be found here.
\nCreate the destination table.
\nNote: 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.
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 this query 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
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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 costs. Many of them are shifting towards Azure Data Explorer (ADX) and taking advantage of the significant benefits it offers to analyze billions of records quickly and cost-effectively.
But sometimes they are already 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 significantly boost performance, reduce costs and improve the quality of insights 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:
Azure Data Explorer is a highly scalable and fully managed data analytics 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.
\n
Power
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 data and perform ad-hoc analytics in seconds.
\nUsers 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.
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 insights and 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
Performance
\nAs 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. ADX can accept hundreds of billions of records a day and store this data reliably for months. ADX is specifically built to handle complex analytics queries on the data and return answers in a matter of seconds, even when the data is a combination of structured, semi-structured (JSON-like nested types), and unstructured (free text).
Price
\nAzure 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 ADX to 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 incurring additional costs. In addition, ADX provides a granular cache policy that customers can set to differentiate between hot data cache and cold data cache, and get even more control over the costs.
\nYou can configure and estimate the costs with our cost estimator.
\n\n
As announced in a separate blog post, we developed the K2Bridge (Kibana-Kusto Bridge), an open-source project that enables you to connect your familiar Kibana’s Discover tab to Azure Data Explorer. Starting with Kibana 6.8, you can store your data in Azure Data Explorer on the back end and use K2Bridge to connect to Kibana. This way, your end-users can keep using Kibana’s Discover tab as their data exploration tool. Connect Kibana to Azure Data Explorer with K2Bridge
\n\n
Azure Data Explorer offers various other exploration and visualization capabilities that take advantage of the rich and built-in analyses options of KQL, including:
\nAre 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 next chapter), and keep 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:
For more information, please refer to the data ingestion overview.
\n\n
When you decide to migrate historical data, it is a 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 not migrate historical 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.
\n
Here we describe two methods to migrate historical data from Elasticsearch using Logstash. See the appendix for a step-by-step tutorial.
\n
Method 1: Logstash and One-click Ingestion/LightIngest
\n\n
Use Logstash to export the data from Elasticsearch into CSV or JSON file(s), and then use Azure Data Explorer’s One-Click Ingestion feature to ingest the data.
\nMethod 2: Using Logstash only (with the output plugin for Azure Data Explorer)
\n\n
Use Logstash as a pipeline for both exporting data from Elasticsearch and ingesting it into Azure Data Explorer. When you use this method, you should manually create the Azure Data Explorer destination table and define the ingestion mapping. (You can automatically generate the destination table and the table mapping by using One-Click Ingestion with sample data, as described in method 1 first, and then use method 2 for the rest of the data)
\nIn this blog post, we talked about the advantages of Azure Data Explorer, went over several visualizations options, including the open-source Kibana-Azure Data Explorer connector, and introduced a variety of ways you can ingest your ongoing data into Azure Data Explorer. Then, we presented two ways to migrate historical data from Elasticsearch to Azure Data Explorer.
In the appendix, you can find two step-by-step sample scenarios for historical data migration.
Please do not hesitate to contact our team or leave a comment if you have any questions or concerns.
# 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}This config file specifies that the ‘input’ for this process is the Elasticsearch cluster, and the ‘output’ is the CSV file.
\n# 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}logstash -f pipeline.conf More information on creating an Azure Data Explorer cluster and database can be found here.
\nCreate the destination table.
\nNote: 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.
.create tables MyStormEvents(StartTime:datetime,EndTime:datetime,EpisodeId:int,EventId:int,State:string,EventType:string) .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 \"}]'bin/logstash-plugin install logstash-output-kustoinput {\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}logstash -f pipeline.conf MyStormEvents | countThe 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 this query 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.
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