Druid batch ingestion

Apache Druid 0.

druid batch ingestion

Refer to the complete list of changes and everything tagged to the milestone for further details. Vectorized query engines for GroupBy and Timeseries queries were introduced in Druid 0. Since then we have extensively tested these engines and feel that the time has come for these improvements to find a wider audience. Note that not all of the query engine is vectorized at this time, but this change makes it so that any query which is eligible to be vectorized will do so. This feature may still be disabled if you encounter any problems by setting druid.

New in Druid 0. Check out the docs for more details. An 'SqlInputSource' has been added in Druid 0. This is a relatively low level ingestion task, and the operator must take care to manually ensure that the correct data is ingested, either by specially crafting queries to ensure no duplicate data is ingested for appends, or ensuring that the entire set of data is queried to be replaced when overwriting.

See the docs for more operational details. A new extension in Druid 0. A new 'contrib' extension has been added for Alibaba Cloud Object Storage Service OSS to provide both deep storage and usage as a batch ingestion input source. Since this is a 'contrib' extension, it will not be packaged by default in the binary distribution, please see community extensions for more details on how to use in your cluster. Another 'contrib' extension new in 0.

Unlike the Amazon Web Services ingestion autoscaling extension, which provisions and terminates instances directly without using an Auto Scaling Group, the GCE autoscaler uses Managed Instance Groups to more closely align with how operators are likely to provision their clusters in GCE. Like other 'contrib' extensions, it will not be packaged by default in the binary distribution, please see community extensions for more details on how to use in your cluster.

Druid 0. Creating and editing lookups is now done with a form to accept user input, rather than a raw text editor to enter the JSON spec. Additionally, clicking the magnifying glass icon next to a lookup will now allow displaying the first values of that lookup. A coordinator API can make it easier to determine if the latest published segments are available for querying. This is similar to the existing coordinator 'loadstatus' API, but is datasource specific, may specify an interval, and can optionally live refresh the metadata store snapshot to get the latest up to date information.

Note that operators should still exercise caution when using this API to query large numbers of segments, especially if forcing a metadata refresh, as it can potentially be a 'heavy' call on large clusters.

Introducing Apache Druid 0.19

Part bug fix, part new feature, Druid native batch once again supports appending new data to existing time chunks when those time chunks were partitioned with 'hash' or 'range' partitioning algorithms.

Note that currently the appended segments only support 'dynamic' partitioning, and when rolling back to older versions that these appended segments will not be recognized by Druid after the downgrade. In order to roll back to a previous version, these appended segments should be compacted with the rest of the time chunk in order to have a homogenous partitioning scheme.

This had the implication that when these segments came online, they did not do so as a complete set, but rather as individual segments, meaning that there would be periods of swapping where results could be queried from an incomplete partition set within a time chunk.

Prior to 0. These changes to the json format should be backwards compatible, however rolling back to a previous version will again make these segments no longer queryable.

A bug in Druid versions prior to 0. This bug would allow segments to load, and effectively randomly balance them in the cluster regardless of what balancer strategy was actually configured if all historicals did not have this value set. This bug has been fixed, but as a result druid. Please be aware of the following issues when upgrading from 0. If you're updating from an earlier version than 0.

8. Druid Native Batch

A Coordinator bug fix as a side-effect now requires druid. While this value should have been set correctly for previous versions, please be sure this value is configured correctly before upgrading your clusters or else segments will not be loaded. The removal of the 'payload' column from the sys.

The druid.My Boulder team has started working with a distributed data store called Druid. I encountered an issue when importing data from a CSV file, and I wanted to share a way I found to do it. Early on, I learned that a distributed data store like Druid is akin to a database, but one where information is stored on multiple nodes. Often, these are non-relational stores, which allow for lightning quick data access.

I decided I wanted to get some hands-on experience with Druid, so I installed it on my laptop. Plus, for the purpose of seeing what Druid is capable of, a batch import of data from a static data table seemed like the easiest way to get started.

I played with their quick-start data for a bit, but there are only a few dozen records and I wanted something more substantial. Also, I wanted to use a dataset that was more personally relatable than Wikipedia edits from a year ago. In particular, I found a set of weather data on Kaggle that caught my eye. The folks at FiveThirtyEight had scraped some data from Weather Underground for some weather stations across the country. This dataset contains daily highs, lows, and precipitation observations.

This triggers Druid to index data in order to create Druid segments, time-partitioned sets of data. Once data has been ingested into segments, you can leverage Druid to slice and dice the data. It took me a lot of trial and error to hit upon the correct JSON syntax, and the main purpose of this post is to share my findings. Any section not mentioned below was left untouched from the quick-start guide. I heavily modified the parser node.

The ingestion documentation lists the high-level nodes that are needed, but I found the documentation sparse in detail beyond that. This is the section that took the most time to get right. I found the errors Druid gave were not intuitive at first.

druid batch ingestion

Fortunately, I have a lot of experience reading stack traces and eventually was able to figure out what was going on without resorting to reading the source code! There were several varieties of objects being null, which I later learned were the result of omissions in my JSON file. I changed the timestamp node to use the date column in the CSV file. I also updated the CSV file to put the date in a format I found easier to read. A word about Druid segments. A segment can be envisioned as a table with columns composed of a timestamp, a set of dimensions, and a set of metrics.

Dimensions are raw data values, whereas metrics are calculated values. Knowing about segments makes the parser node easy to understand. The columns array lists the columns in the CSV source file, in order. The timestampSpecdimensionSpecand metricsSpec tells Druid how to ingest the source values into a segment.

After getting all these sections straight, I hit a more intuitive error. It turns out that with this configuration, Druid could not make sense of the header row in the CSV file. Rather than try to find out how to tell Druid to ignore the header, I simply removed it from the CSV file.

And thus, after a dozen tries, I got the data into Druid! Now that I have ingestion working, the next step is to choose some more interesting metrics to measure.Brian Chesky co-founded peer-to-peer room and home rental company Airbnb with Nathan Blecharczyk and Joe Gebbia in Now, almost 11 years later, Airbnb has been used by more than million people in 81, cities in countries. At Airbnb, dashboard analytics plays a crucial role in real-time tracking and monitoring of various aspects of business and systems.

As a result, the timeliness of these dashboards is critical to the daily operation of Airbnb. And, Druid serves as the best platform for ingestion, streaming and handling of large volumes of data while ensuring there are no latencies.

So, Druid helps Airbnb in tackling these challenges and keeping the services afloat even during unwanted shutdowns. At Airbnbtwo Druid clusters are running in production. Two separate clusters allow dedicated support for different uses, even though a single Druid cluster can handle more data sources than what we need.

Druid clusters are relatively small and low cost compared to other service clusters like HDFS and Presto. For dashboards using systems like Hive and Presto at query time, data aggregation takes a long time. The storage format makes it difficult for repeated slicing of the data. The dashboards built on top of Druid can be noticeably faster than those built on others systems.

Compared to Hive and Presto, Druid can be an order of magnitude faster. With high inflows of data, any breakdown will take a toll on the systems. To make the systems breakdown-ready, Druid architecture is well separated out into different components for ingestion, serving, and overall coordination. Whereas, real-time analytics is implemented through Spark. Apache Superset, an open source data viz of Airbnb serves as the interface for users to visualize the results of their analytics queries.

Druid allows individual teams and data scientists to easily define how the data their application or service produces should be aggregated and exposed as a Druid data source. Every data segment needs to be ingested from MapReduce jobs first before it is available for queries. Druid queries are much faster than other systems is that there is a cost imposed on ingestion. This works great in a write-once-read-multiple-times model, and the framework only needs to ingest new data on a daily basis.

One of the issues we deal with is the growth in the number of segment files that are produced every day that need to be loaded into the cluster. Segment files are the basic storage unit of Druid data, that contains the pre-aggregated data ready for serving. Some data needs recomputation, resulting in a large number of segment files that need to be loaded at once onto the cluster. Ingested segments are loaded by coordinators sequentially in a single thread, centrally.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I also issued time boundary query and everything worked fine.

druid batch ingestion

We had the same problem, the index task is not very optimize to ingest large amount of data. They write it in the documentation: "They are however, slow when the data volume exceeds 1G. The Index Hadoop Task is the best solution if you need to batch-ingest large amount of data.

It scales well and it's significantly faster. Recent work on druid did serious improvement to the indexing task. Also both Index Hadoop task and index task do the same thing. Learn more. Apache Druid batch ingestion - low performance of index task Ask Question.

Asked 4 years, 9 months ago. Active 4 years, 7 months ago. Viewed 2k times. Is it possible by: change of segment granuality? Please help. Aacini Active Oldest Votes. Slim Bouguerra Slim Bouguerra 1 1 silver badge 8 8 bronze badges. The docs indicate the index task is not intended for production workloads, is it it really identical? I thought it does not scale well since there is no scheduler? It takes around hrs to ingest around 20GB of data. Sign up or log in Sign up using Google.

Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown.I was used druid since 2 months for real time ingestion from a kafka topic. I did lot of test :. Support Questions. Find answers, ask questions, and share your expertise.

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Former HCC members be sure to read and learn how to activate your account here. All forum topics Previous Next. Druid batch ingestion best pratice -- Kafka topic from beginning. Labels: Apache Kafka Druid. Hi, I was used druid since 2 months for real time ingestion from a kafka topic.

I did lot of test : I consume my topic from begin with spark streaming and use tranquility for real time ingestion.

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My topic contain lot of message and when i consume from begin i have more running task real time in druid and it's can't be done except i configure my window period and i loss more message I try with a kafka connect druid but i have same problem I read lot of article about druid and i understand that for historical data the best way is to do batch ingstion.

I start with index batch ingestion and then with hadoop batch ingestion. Any of this method work with me unless i am not well using batch ingestion configuration. Please for people that work long time with druid how can i resolve batch ingestion problem? Thanks in advance. Reply Views. Tags 4. Already a User? Sign In. Don't have an account? Coming from Hortonworks?By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I want to write Spark batch results data to the Apache Druid. Druid runs Map-Reduce jobs in the same cluster. But I only want to use Druid as a data storage. I want to aggregate data external Spark cluster, then send it to the Druid cluster.

Druid has Tranquility for real-time ingestion. I can send batch data using Tranquility, but this is not efficient. How can I send batch results to the Druid efficiently?

We have been using this mechanism for indexing data but there is no such restriction of windowPeriod in that. It takes even older timestamp. But if a shard is already finalized, this ends up creating new shards in same segment. Auto compaction works well for this option too. Learn more. Ask Question. Asked 11 months ago. Active 11 months ago. Viewed times. Spark gives u a connector to write to kafka. Another way.

druid batch ingestion

Kafka options is not good. Because, event times generally older than windowPartition. Another options seems good. I can write results as parquet format to the Druid's HDFS, then create a hdfs index to convert parquet to segments. This seems a good results, because data already aggregated, and just convert it to the segments. This seems it'll consumes lower resources than other options. HDFS way is the fastest and the most efficient way.

Btw, this way of ingestion replaces the existing segments in druid so make sure that u ingest the full data, not just the delta rows. Kafka on the other hand --works good to ingest just the delta rows; btw didnt understand window partition from ur reply --elaborate it. I tried to say window period. In real time ingestion, druid expects a window period.

If any time of the received events is out of the specified window, this event ignored. As a results, results of batch analysis's times is always out of the window period I assume window period is 10 minutes, and it can not be too large.Please refer to our Hadoop-based vs.

To run either kind of native batch indexing task, write an ingestion spec as specified below. This page contains reference documentation for native batch ingestion. For a walk-through instead, check out the Loading a file tutorial, which demonstrates the "simple" single-task mode. This task only uses Druid's resource and doesn't depend on other external systems like Hadoop.

The supervisor task splits the input data and creates worker tasks to process those splits. The created worker tasks are issued to the Overlord so that they can be scheduled and run on MiddleManagers or Indexers. Once a worker task successfully processes the assigned input split, it reports the generated segment list to the supervisor task. The supervisor task periodically checks the status of worker tasks.

If one of them fails, it retries the failed task until the number of retries reaches the configured limit. If all worker tasks succeed, it publishes the reported segments at once and finalizes ingestion. The detailed behavior of the Parallel task is different depending on the partitionsSpec. See each partitionsSpec for more details. To use this task, the inputSource in the ioConfig should be splittable and maxNumConcurrentSubTasks should be set to larger than 1 in the tuningConfig.

The supported splittable input formats for now are:. Some other cloud storage types are supported with the legacy firehose. The below firehose types are also splittable. Note that only text formats are supported with the firehose. If you specify intervals explicitly in your dataSchema's granularitySpecbatch ingestion will lock the full intervals specified when it starts up, and you will learn quickly if the specified interval overlaps with locks held by other tasks e.

Apache Druid architectural overview

Otherwise, batch ingestion will lock each interval as it is discovered, so you may only learn that the task overlaps with a higher-priority task later in ingestion. If you specify intervals explicitly, any rows outside the specified intervals will be thrown away. We recommend setting intervals explicitly if you know the time range of the data so that locking failure happens faster, and so that you don't accidentally replace data outside that range if there's some stray data with unexpected timestamps.

The tuningConfig is optional and default parameters will be used if no tuningConfig is specified. See below for more details.

The split hint spec is used to give a hint when the supervisor task creates input splits. Note that each worker task processes a single input split.


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