Tutorial: Using Apache Druid and Imply With Google Cloud Dataproc For Hadoop Indexing

Jun 06, 2019
Rommel Garcia

I. Introduction

Lately we have noticed a surge in demand for GCP amongst enterprises. The volume of data moving to the cloud is growing, and part of that data is of very high value, with analysts and operators requiring lightning fast access in order for the business to identify and act on important trends. New technologies that enable low-latency queries at scale in the cloud will need to be adopted. One of them is Apache Druid (incubating).

Druid is a distributed, realtime database that is designed to deliver sub-second query response on batch and streaming data at petabyte scale. On top of Druid, Imply provides an interactive query-and-visualize UI so non-technical business operators can iteratively explore the data and quickly discover opportunities for improvement.

Imply was founded by the authors of Druid and delivers an enterprise-ready Druid solution – including visualization, management and security – to customers across the globe. Imply enables enterprises to operate on-prem or via their cloud platform of choice, including GCP.

To help you get to know GCP and Druid, the tutorial below will walk you through how to install and configure Druid to work with Dataproc (GCP’s managed Hadoop offering) for Hadoop Indexing. Then it will show you how to ingest and query data as well.

Hadoop Indexing using Druid is an important use case since the majority of enterprises today have Hadoop deployments but Hadoop does not natively support indexing or low-latency real-time queries.


There are several key requirements that need to be completed before Imply and Dataproc are deployed.

Service Account

The service account that will be deploying GCP vms for Imply and Dataproc environment must have the following roles set up.

  • Compute Admin
  • Dataproc Administrator
  • Owner
  • Storage Admin

Here’s a sample entry from the GCP IAM page.

When creating the GCP compute vms, make sure to choose the service account you are provisioning with the proper roles and that the Cloud API access scopes is set to “Allow full access to all Cloud APIs”. Do not use the default GCE service account.

Network & Firewall

Ensure that the vms provisioned for Imply and Dataproc are visible to one another. It is recommended that you put Dataproc in its own subnet. A high speed network is ideal. For large, batch ingest in the double-digit TB range or larger, it is best to have 100G of bandwidth, specially for time-sensitive processing. For time-insensitive ingest, between 10G to 40G is sufficient.

The only thing to consider for the firewall is to make sure that you are providing IP ranges for each rule along with its ports/range of ports. The default rules are sufficient but something specific needs to be setup, specifically for Pivot. As shown below, all except for “pivot-2” rule are defaults. The IPs that would need access to Pivot (port 9095), Coordinator (8081), Overlord (8090), and Broker (8082) need to be defined. The term “Ingress” is equivalent to “Inbound” and “Egress” is the same as “Outbound”.

Also make sure to allow HTTP traffic.

Google Cloud Storage

Login to your GCP account and validate that you can create folders, upload files, and delete files in your assigned bucket.

Google Cloud SDK

You can download the Google Cloud SDK here. There are two tools from this SDK that you will be using on a regular basis – gcloud and gsutil. Install the SDK and follow the instructions for initializing it. It will eventually ask you to authenticate yourself with your gmail account.

There are several options to login to the vms for Imply and Dataproc. You can check all the options here but often it is easier to use the OS Login as this doesn’t require managing ssh keys. Before deploying vms, make sure to update your Metadata key/value pair as shown below. The key is “enable-oslogin” and value is “TRUE”. This is at the project level. So if you have multiple projects and you want to use OS Login option, make sure to go to each project and enter the same information. If you do have vms already deployed prior to enabling OS Login metadata update, you can go and edit the vm configuration under “Custom metadata” and enter the same key/value pair. It’s not necessary to restart the vm as this change will take effect immediately.

Here are the two steps to login into your vms:

  1. Go to your vm and click on SSH drop down.
  2. Choose “View gcloud command” and copy the command and run it from your host terminal.

II. Imply Installation

Create your vms with the proper configuration that will meet your use case. GCP provides you the ability to expand the memory up to 624GB per vm. A sample setup is shown below.

Before installing Imply, make sure that the vms can read/write from/to your GCS bucket. You can use the sample command format below. Most of the commands from here on out will require “sudo” level access.

To write to a bucket:
sudo gsutil cp /path_to_file gs://some_bucket

To read data from a bucket:
sudo gsutil cp gs://some_bucket /path_to_file_directory

If both commands are successful, your vms have correct access to your buckets.

At this point, you can now install Imply. Use the latest version available here. Update all the necessary Druid configuration files.

III. Dataproc Installation

Make sure to deploy Dataproc in the same GCP region as Imply. Choose the right number of CPUs and sufficient memory to meet the SLA for hadoop ingest into Druid. One thing to note is that the “Local SSDs (0-8)” field is for storing temporary/staging data when Hadoop jobs are running. Choose the appropriate number of disk for the use case.

The Advanced section below highlights Dataproc version 1.3. This version (or greater) is what is needed as it ships with druid-google-extensions and the gcs-connector-hadoop that is compatible with Imply 2.8.x and above.

You can leave the defaults in the last section. If you do have to automate the configuration after you install the cluster, you can define your initialization scripts stored in the GCS bucket and they will be executed upon completion of the vm provisioning.

Run a test to read files into the bucket using the hadoop command below. It should list all directories/file underneath it.

hadoop fs -ls gs://your_bucket

Dataproc has some configuration files, along with the packaged open source Druid jars that are needed to be copied over to Imply.

IV. Imply and Dataproc Integration

Dataproc Steps

There are several things that needs to be copied from Dataproc and pushed over to Imply. Here is the list; make sure to apply proper permissions after copying them.

  • core-site.xml
  • hdfs-site.xml
  • mapred-site.xml
  • yarn-site.xml

Create a bucket that will store these configuration files. You can use a command similar to the one below to copy them over. Login to one of the Dataproc vms and run the command.

sudo gsutil cp /usr/lib/hadoop/etc/hadoop/*-site.xml gs://your_bucket

This will copy over all the Hadoop configurations to your bucket. The next step is to copy the druid-google-extensions folder, google client/api and gcs-connector jars to your bucket. The gcs-connector and the rest of the google jars also needs to be copied over to all Dataproc vms under /usr/lib/hadoop/lib/.

sudo gsutil cp \
/opt/druid/apache-druid-0.13.0-incubating/extensions/druid-google-extensions \

sudo gsutil cp /usr/lib/hadoop/lib/gcs-connector.jar gs://your_bucket

sudo cp \
 /opt/druid/apache-druid-0.13.0-incubating/extensions/druid-google-extensions/*google* \

sudo cp \
.jar /usr/lib/hadoop/lib/

Update the hadoop-env.sh script to include the gcs-connector.jar in the HADOOP_CLASSPATH. Your entry should look like this. It will automatically be picked up by Dataproc.


Imply Steps

First and foremost, remove the druid-hdfs-storage extension if you have it in your loadList. Your loadList from your common.runtime.properties file should look similar to below.


If you don’t remove the hdfs extension, it will show an error below which will not allow your Dataproc job to run and finish.

1) Error injecting constructor, java.lang.IllegalArgumentException: Can not create a Path from an empty string
  at org.apache.druid.storage.hdfs.HdfsDataSegmentKiller.<init>(HdfsDataSegmentKiller.java:47)
  while locating org.apache.druid.storage.hdfs.HdfsDataSegmentKiller
  at org.apache.druid.storage.hdfs.HdfsStorageDruidModule.configure(HdfsStorageDruidModule.java:94) (via modules: com.google.inject.util.Modules$OverrideModule -> org.apac
  while locating org.apache.druid.segment.loading.DataSegmentKiller annotated with @com.google.inject.multibindings.Element(setName=,uniqueId=146, type=MAPBINDER, keyType=

Run the following commands to copy over all the configurations and jars from the bucket and put them in their respective location. All of these commands should be applied to all Imply vms. Ensure proper permissions are set.

sudo gsutil cp gs://your_bucket/gcs-connector.jar  <imply_home_dir>/dist/druid/lib

sudo gsutil cp gs://your_bucket/druid-google-extensions \
<imply_home_dir>/dist/druid/extensions \

sudo gsutil cp gs://your_bucket/gcs-connector.jar \  

sudo gsutil cp gs://your_bucket/*-site.xml <imply_home_dir>/conf/druid/_common

The default Hadoop working tmp directory from Imply doesn’t exist in Dataproc so the MiddleManager runtime.properties need to be updated to reflect the Hadoop working tmp directory (/hadoop/tmp) in Dataproc. The update property file should look like this.


Update the common.runtime.properties as shown below.

# For GCS as Deep Storage
# Cloudfiles storage configuration

# Indexing service logs

V. Testing

There are two levels of testing required to validate initial setup before doing hadoop-indexing using Dataproc.

Test 1: Imply HTTP Ingest Test From Pivot

Go to Pivot UI http://pivot_ip:9095. Then follow the steps below to do a native batch ingest and storing segments in GCS bucket.

  1. Click on the Load button
  2. This will show several options. Choose Wikipedia Edits.
  3. This will pull up a series of pages which are ok to leave with default settings.
    1. Click Sample and continue.
    2. Click Yes this is the data I wanted
    3. Click Configure columns.
    4. Click Additional Configs
    5. Click Review config
    6. Click Start loading data
      This will now ingest the wikipedia edit files, create the segment and store them in the GCS bucket. When successful, it should look something like this.

The historicals also should have the Wikipedia segments.

This completes the batch ingest into GCS.

Test 2: Imply Hadoop Indexer Test

We will be using the same Wikipedia dataset for this test. Copy over the compressed Wikipedia file locally onto your machine and load it to GCS.

$ wget https://static.imply.io/data/wikipedia.json.gz .
$ gunzip wikipedia.json.gz
$ gsutil cp wikipedia.json gs://your_bucket

Follow the steps below to run the Hadoop indexer.

  1. Click the Load button
  2. Choose Other (batch)
  3. This will pull up default ingest spec as shown below.
    Replace it by using the ingest spec below. Notice that the jobProperties have defined the mapper and reducer java opts to use UTC timezone. If you don’t have this set, Druid will throw an error “No data exists”. Update your “paths” field to point to your file/directory correctly in GCS. { "type" : "index_hadoop", "spec" : { "dataSchema" : { "dataSource" : "wikipedia", "parser" : { "type" : "hadoopyString", "parseSpec" : { "format" : "json", "dimensionsSpec" : { "dimensions" : [ "isRobot", "diffUrl", { "name": "added", "type": "long" }, "channel", "flags", { "name": "delta", "type": "long" }, "isUnpatrolled", "isNew", { "name": "deltaBucket", "type": "long" }, "isMinor", "isAnonymous", { "name": "deleted", "type": "long" }, "namespace", "comment", "page", { "name": "commentLength", "type": "long" }, "user", "countryIsoCode", "regionName", "cityName", "countryName", "regionIsoCode", { "name": "metroCode", "type": "long" } ] }, "timestampSpec": { "column": "timestamp", "format": "iso" } } }, "metricsSpec" : [], "granularitySpec" : { "type": "uniform", "segmentGranularity": "DAY", "queryGranularity": { "type": "none" }, "rollup": false, "intervals": null } }, "ioConfig" : { "type" : "hadoop", "inputSpec" : { "type" : "static", "paths" : "gs://your_bucket/wikipedia.json" } }, "tuningConfig" : { "type" : "hadoop", "partitionsSpec" : { "type" : "hashed", "targetPartitionSize" : 5000000 }, "forceExtendableShardSpecs" : true, "jobProperties" : { "mapreduce.job.classloader": "true", "mapreduce.job.user.classpath.first": "true", "mapreduce.map.java.opts":"-Duser.timezone=UTC -Dfile.encoding=UTF-8", "mapreduce.reduce.java.opts":"-Duser.timezone=UTC -Dfile.encoding=UTF-8" } } } }
  4. You should be able to see this once the Hadoop indexing is successful. All the segments should exists in your GCS bucket and in historical as well. This completes the full functional testing for Imply and Dataproc.
  5. Now we can query the data using Imply Pivot by clicking on the “Visualize” button. This will take you to the “slice-n-dice” page where you can start discovering trends, patterns, etc.
    Visualize Page:
    Showing trends for edits by channel and comment:
    Running SQL queries in the SQL View:

VI. Troubleshooting

Coordinator Log

  • com.google.inject.Guice - UnknownHostExceptionX
    If you get the exception below, that means that you have an old copy of the -site.xml files from dataproc. A new dataproc cluster has been created and all its -site.xml files have to be copied over to the Imply cluster.   2019-03-16T19:54:08,402 INFO [main] com.google.inject.Guice - An exception was caught and reported. Message: java.net.UnknownHostException: some-host-m java.lang.IllegalArgumentException: java.net.UnknownHostException: some-host-m

MiddleManager Log

  • No buckets?? It seems that there is no data to index.
    The log below indicates that the Hadoop working directory for Druid doesn’t exist. You can check this in druid.indexer.task.hadoopWorkingPath and make sure that the path exists in hadoop file system. 2019-03-19T15:09:38,646 INFO [task-runner-0-priority-0] org.apache.druid.indexer.DetermineHashedPartitionsJob - Path[var/druid/hadoop-tmp/wikipedia-2/2019-03-19T150739.100Z_1c16425db4864050bbf859979c3da5b2/20160627T000000.000Z_20160628T000000.000Z/partitions.json] didn't exist!? 2019-03-19T15:09:38,646 INFO [task-runner-0-priority-0] org.apache.druid.indexer.DetermineHashedPartitionsJob - DetermineHashedPartitionsJob took 106649 millis 2019-03-19T15:09:38,647 INFO [task-runner-0-priority-0] org.apache.druid.indexer.JobHelper - Deleting path[var/druid/hadoop-tmp/wikipedia-2/2019-03-19T150739.100Z_1c16425db4864050bbf859979c3da5b2] 2019-03-19T15:09:38,781 INFO [task-runner-0-priority-0] org.apache.druid.indexing.common.actions.RemoteTaskActionClient - Performing action for task[index_hadoop_wikipedia-2_2019-03-19T15:07:39.100Z]: LockListAction{} 2019-03-19T15:09:38,784 INFO [task-runner-0-priority-0] org.apache.druid.indexing.common.actions.RemoteTaskActionClient - Submitting action for task[index_hadoop_wikipedia-2_2019-03-19T15:07:39.100Z] to overlord: [LockListAction{}]. 2019-03-19T15:09:38,802 INFO [task-runner-0-priority-0] org.apache.druid.indexing.common.task.HadoopIndexTask - Setting version to: 2019-03-19T15:07:39.112Z 2019-03-19T15:09:39,075 INFO [task-runner-0-priority-0] org.apache.druid.indexing.common.task.HadoopIndexTask - Starting a hadoop index generator job... 2019-03-19T15:09:39,126 INFO [task-runner-0-priority-0] org.apache.druid.indexer.path.StaticPathSpec - Adding paths[gs://imply-walmart/test/wikipedia-2016-06-27-sampled.json] 2019-03-19T15:09:39,130 INFO [task-runner-0-priority-0] org.apache.druid.indexer.HadoopDruidIndexerJob - No metadataStorageUpdaterJob set in the config. This is cool if you are running a hadoop index task, otherwise nothing will be uploaded to database. 2019-03-19T15:09:39,189 ERROR [task-runner-0-priority-0] org.apache.druid.indexing.common.task.HadoopIndexTask - Encountered exception in HadoopIndexGeneratorInnerProcessing.
  • com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem not found
    When a MR job is run and the error below occurs, that means that the gcs-connector.jar is not in the HADOOP_CLASSPATH. 2019-03-19T13:54:59,913 ERROR [task-runner-0-priority-0] org.apache.druid.indexing.common.task.HadoopIndexTask - Got invocation target exception in run(), cause: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem not found

Historical Log

  • com.google.inject.ProvisionException
    The error below means that you have hdfs-storage-extension in your loadList. com.google.inject.ProvisionException: Unable to provision, see the following errors: 1) Error injecting constructor, java.lang.IllegalArgumentException: Can not create a Path from an empty string at org.apache.druid.storage.hdfs.HdfsDataSegmentKiller.<init>(HdfsDataSegmentKiller.java:47) while locating org.apache.druid.storage.hdfs.HdfsDataSegmentKiller at org.apache.druid.storage.hdfs.HdfsStorageDruidModule.configure(HdfsStorageDruidModule.java:94) (via modules: com.google.inject.util.Modules$OverrideModule -> org.apache.druid.storage.hdfs.HdfsStorageDruidModule) while locating org.apache.druid.segment.loading.DataSegmentKiller annotated with @com.google.inject.multibindings.Element(setName=,uniqueId=147, type=MAPBINDER, keyType=java.lang.String) at org.apache.druid.guice.Binders.dataSegmentKillerBinder(Binders.java:41) (via modules: com.google.inject.util.Modules$OverrideModule -> org.apache.druid.storage.hdfs.HdfsStorageDruidModule -> com.google.inject.multibindings.MapBinder$RealMapBinder) while locating java.util.Map<java.lang.String, org.apache.druid.segment.loading.DataSegmentKiller> for the 1st parameter of org.apache.druid.segment.loading.OmniDataSegmentKiller.<init>(OmniDataSegmentKiller.java:38) while locating org.apache.druid.segment.loading.OmniDataSegmentKiller at org.apache.druid.cli.CliPeon$1.configure(CliPeon.java:218) (via modules: com.google.inject.util.Modules$OverrideModule -> com.google.inject.util.Modules$OverrideModule -> org.apache.druid.cli.CliPeon$1) while locating org.apache.druid.segment.loading.DataSegmentKiller for the 5th parameter of org.apache.druid.indexing.common.TaskToolboxFactory.<init>(TaskToolboxFactory.java:113) at org.apache.druid.cli.CliPeon$1.configure(CliPeon.java:201) (via modules: com.google.inject.util.Modules$OverrideModule -> com.google.inject.util.Modules$OverrideModule -> org.apache.druid.cli.CliPeon$1) while locating org.apache.druid.indexing.common.TaskToolboxFactory for the 1st parameter of org.apache.druid.indexing.overlord.SingleTaskBackgroundRunner.<init>(SingleTaskBackgroundRunner.java:95) at org.apache.druid.cli.CliPeon$1.configure(CliPeon.java:240) (via modules: com.google.inject.util.Modules$OverrideModule -> com.google.inject.util.Modules$OverrideModule -> org.apache.druid.cli.CliPeon$1) while locating org.apache.druid.indexing.overlord.SingleTaskBackgroundRunner while locating org.apache.druid.indexing.overlord.TaskRunner for the 4th parameter of org.apache.druid.indexing.worker.executor.ExecutorLifecycle.<init>(ExecutorLifecycle.java:79) at org.apache.druid.cli.CliPeon$1.configure(CliPeon.java:224) (via modules: com.google.inject.util.Modules$OverrideModule -> com.google.inject.util.Modules$OverrideModule -> org.apache.druid.cli.CliPeon$1) while locating org.apache.druid.indexing.worker.executor.ExecutorLifecycle

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Tales at Scale Podcast Kicks off with the Apache Druid Origin Story

Tales at Scale cracks open the world of analytics projects and shares stories from developers and engineers who are building analytics applications or working within the real-time data space. One of the key...

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May 11, 2023

Real-time Analytics Database uses partitioning and pruning to achieve its legendary performance

Apache Druid uses partitioning (splitting data) and pruning (selecting subset of data) to achieve its legendary performance. Learn how to use the CLUSTERED BY clause during ingestion for performance and high...

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May 10, 2023

Easily embed analytics into your own apps with Imply’s DBaaS

This blog explains how developers can leverage Imply Polaris to embed robust visualization options directly into their own applications without them having to build a UI. This is super important because consuming...

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May 09, 2023

Building an Event Analytics Pipeline with Confluent Cloud and Imply’s real time DBaaS, Polaris

Learn how to set up a pipeline that generates a simulated clickstream event stream and sends it to Confluent Cloud, processes the raw clickstream data using managed ksqlDB in Confluent Cloud, delivers the processed...

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May 08, 2023

Real time DBaaS comes to Europe

We are excited to announce the availability of Imply Polaris in Europe, specifically in AWS eu-central-1 region based in Frankfurt. Since its launch in March 2022, Imply Polaris, the fully managed Database-as-a-Service...

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May 07, 2023

Stream big, think bigger—Analyze streaming data at scale in 2023

Imply is predicting the next "big thing" in 2023 will be analyzing streaming data in real time (and Druid is built for just that!)

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May 07, 2023

Should You Build or Buy Security Analytics for SecOps?

When should you build—or buy—a security analytics platform for your environment? Here are some common considerations—and how Apache Druid is the ideal foundation for any in-house security solution.

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May 05, 2023

Introducing Apache Druid 25.0

Apache Druid 25.0 contains over 293 updates from over 56 contributors.

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May 03, 2023

Druid and SQL syntax

This is a technical blog, which summarises the process of extending the Druid's SQL grammar for ingestion and delves into the nitty gritty of Calcite.

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May 02, 2023

Native support for semi-structured data in Apache Druid

Describes a new feature- ingest complex data as is into Druid- massive improvement in developer productivity

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May 01, 2023

Real-Time Analytics with Imply Polaris: From Setup to Visualization

Imply Polaris offers reduced operational overhead and elastic scaling for efficient real-time analytics that helps you unlock your data's potential.

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May 01, 2023

Datanami Award

Apache Druid won Datanami's 2022 Readers’ and Editors’ Choice Awards for Reader's Choice "Best Data and AI Product or Technology: Analytics Database".

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Apr 30, 2023

Alerting and Security Features in Polaris

Describes new features - alerts and some security features- and how Imply customers can leverage it

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Apr 29, 2023

Ingestion from Amazon Kinesis and S3 into Imply Polaris

Imply Polaris now supports data ingestion from Amazon Kinesis and Amazon S3

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Apr 27, 2023

Getting the Most Out of your Data

Ingesting data from one table to another is easy and fast in Imply Polaris!

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Apr 26, 2023

Combating financial fraud and money laundering at scale with Apache Druid

Learn how Apache Druid enables financial services firms and FinTech companies to get immediate insights from petabytes-plus data volumes for anti-fraud and anti-money laundering compliance.

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Apr 26, 2023

What’s new in Imply – December 2022

This is a what's new to Imply in Dec 2022. We’ve added two new features to Imply Polaris to make it easier for your end users to take advantage of real-time insights.

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Apr 25, 2023

What’s New in Imply Polaris – November 2022

This blog provides an overview for the new features, functionality, and connectivity to Imply Polaris for November 2022.

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Apr 24, 2023

Imply Pivot delivers the final mile for modern analytics applications

This blog is focused on how Imply Pivot delivers the final mile for building an anlaytics app. It showcases two customer examples - Twitch and ironsource.

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Apr 23, 2023

Why Analytics Need More than a Data Warehouse

For decades, analytics has been defined by the standard reporting and BI workflow, supported by the data warehouse. Now, 1000s of companies are realizing an expansion of analytics beyond reporting, which requires...

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Apr 21, 2023

Why Open Source Matters for Databases

Apache Druid is at the heart of Imply. We’re an open source business, and that’s why we’re committed to making Druid the best open source database for modern analytics applications

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Apr 20, 2023

Ingestion from Confluent Cloud and Kafka in Polaris

How to ingest data into Imply Polaris from Confluent Cloud and from Apache Kafka

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Apr 18, 2023

What Makes a Database Built for Streaming Data?

For an analytics app to handle real-time, streaming sources, it must be built for streaming data. Druid has 3 essential features for stream data.

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Oct 12, 2022

SQL-based Transformations and JSON Columns in Imply Polaris

You can easily do data transformations and manage JSON data with Imply Polaris, both using SQL.

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Oct 06, 2022

Approximate Distinct Counts in Imply Polaris

When it comes to modern data analytics applications, speed is of the utmost importance. In this blog we discuss two approximation algorithms which can be used to greatly enhance speed with only a slight reduction...

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Sep 20, 2022

The next chapter for Imply Polaris: celebrating 250+ accounts, continued innovation

Today we announced the next iteration of Imply Polaris, the fully managed Database-as-a-Service that helps you build modern analytics applications faster, cheaper, and with less effort. Since its launch in...

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Sep 20, 2022

Introducing Imply’s Total Value Guarantee for Apache Druid

Apache Druid 24.0 contains 450 updates and new features, major performance enhancements, bug fixes, and major documentation improvements

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Sep 16, 2022

Introducing Apache Druid 24.0

Apache Druid 24.0 contains 450 updates and new features, major performance enhancements, bug fixes, and major documentation improvements

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Aug 16, 2022

Using Imply Pivot with Druid to Deduplicate Timeseries Data

Imply Pivot offers multi step aggregations, which is valuable for timeseries data where measures are not evenly distributed in time.

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Jul 21, 2022

A Look Under the Surface at Polaris Security

We have taken a security-first approach in building the easiest real-time database for modern analytics applications.

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Jul 14, 2022

Upserts and Data Deduplication with Druid

A look at what can be done with Druid for upserts and data deduplication.

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Jul 01, 2022

What Developers Can Build with Apache Druid

We obviously talk a lot about #ApacheDruid on here. But what are folks actually building with Druid? What is a modern analytics application, exactly? Let's find out

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Jun 29, 2022

When Streaming Analytics… Isn’t

Nearly all databases are designed for batch processing, which leaves three options for stream analytics.

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Jun 29, 2022

Apache Druid vs. Snowflake

Elasticity is important, but beware the database that can only save you money when your application is not in use. The best solution will have excellent price-performance under all conditions.

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Jun 22, 2022

Druid 0.23 – Features And Capabilities For Advanced Scenarios

Many of Druid’s improvements focus on building a solid foundation, including making the system more stable, easier to use, faster to scale, and better integrated with the rest of the data ecosystem. But for...

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Jun 22, 2022

Introducing Apache Druid 0.23

Apache Druid 0.23.0 contains over 450 updates, including new features, major performance enhancements, bug fixes, and major documentation improvements.

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Jun 20, 2022

An Opinionated Guide to Component APIs

We have collected a number of guidelines for React component APIs that make components more predictable in terms of behavior and performance.

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Jun 10, 2022

Druid Architecture & Concepts

In a world full of databases, learn how Apache Druid makes real-time analytics apps a reality in this Whitepaper from Imply

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May 25, 2022

3 decisions that shaped the Polaris UI

Imply Polaris is a fully managed database-as-a-service for building realtime analytics applications. John is the tech lead for the Polaris UI, known internally as the Unified App. It began with a profound question:...

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May 19, 2022

How Imply Polaris takes a security-first approach

A primer for developers on security tools and controls available in Imply Polaris

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May 17, 2022

Imply Raises $100MM in Series D funding

There is a new category within data analytics emerging which is not centered in the world of reports and dashboards (the purview of data analysts and data scientists), but instead centered in the world of applications...

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May 11, 2022

Imply Named “Cool Database Vendor” by CRN

There can’t be one database good at everything. When it comes to real-time analytics, you need a database built for it.

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May 11, 2022

Living the Stream

We are in the early stages of a stream revolution, as developers build modern transactional and analytic applications that use real-time data continuously delivered.

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May 02, 2022

Migrating Data from ClickHouse to Imply Polaris

In this blog, we’ll review the simple steps to export data from ClickHouse in a format that is easy to ingest into Polaris.

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Apr 18, 2022

Apache Druid vs. ClickHouse

Developers and architects must look beyond query performance to understand the operational realities of growing and managing a high performance database and if it will consume their valuable time.

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Apr 06, 2022

Java Keytool, TLS, and Zookeeper Security

Lean the basics of Public Key Infrastructure (PKI) as it relates to Druid and Zookeeper security.

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Apr 01, 2022

Building high performance logging analytics with Polaris and Logstash

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

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Apr 01, 2022

For April 1st: a New Description of Apache Druid from Our Youngest Technical Architect

A simple set of instructions to deploy Apache Druid on minikube using minio for local deep storage on your laptop.

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Mar 24, 2022

Distributed by Nature: Druid at Scale

Horizontal scaling is the key to performance at scale, which is why every database claims this. You should investigate, though, to see how much effort it takes, especially compared to Apache Druid.

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Mar 23, 2022

Atomic Replace in Polaris

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

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Mar 22, 2022

Announcing Imply Polaris

Today, we're excited to announce a major leap forward in ease-of-use with the introduction of Imply Polaris, our fully-managed, database-as-a-service.

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Mar 22, 2022

Building Analytics for External Users is a Whole Different Animal

Analytics aren’t just for internal stakeholders anymore. If you’re building an analytics application for customers, then you’re probably wondering…what’s the right database backend? 

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Mar 07, 2022

Clustered Apache Druid® on your Laptop – Easy!

A simple set of instructions to deploy Apache Druid on minikube using minio for local deep storage on your laptop.

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Mar 01, 2022

Why Data Needs More than CRUD

After over 30 years of working with data analytics, we’ve been witness (and sometimes participant) to three major shifts in how we find insights from data - and now we’re looking at the fourth.

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Mar 01, 2022

The Rise of a New Analytics Hero in 2022

Every year industry pundits predict data and analytics becoming more valuable the following year. But this doesn’t take a crystal ball to predict. There’s instead something much more interesting happening...

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Mar 01, 2022

A new shape for Apache Druid

Today, I'm prepared to share our progress on this effort and some of our plans for the future. But before diving further into that, let's take a closer look at how Druid's core query engine executes queries,...

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Feb 11, 2022

Product Update: SSO, Cluster level authorization, OAuth 2.0 and more security features

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

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Feb 04, 2022

Multi-dimensional range partitioning

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

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Dec 12, 2021

Log4Shell Vulnerability and Mitigation

A critical vulnerability has recently been discovered in Apache Log4j, a popular logging library for Java projects.

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Nov 22, 2021

Druid Nails Cost Efficiency Challenge Against ClickHouse & Rockset

To make a long story short, we were pleased to confirm that Druid is 2 times faster than ClickHouse and 8 times faster than Rockset with fewer hardware resources!.

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Nov 09, 2021

Unveiling Project Shapeshift Nov. 9th at Druid Summit 2021

There is a new category within data analytics emerging which is not centered in the world of reports and dashboards (the purview of data analysts and data scientists), but instead centered in the world of applications...

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Oct 25, 2021

How we made long-running queries work in Apache Druid

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

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