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Data sizes are growing rapidly, and this has forced enterprises into re-designing their data architectures to keep up. Because of this, many companies are discovering that they, in fact, require several components to manage their data: a storage layer to store it, a governance layer to manage it, and a query layer to gain business insights from it.

These three layers (storage, governance, and query) become increasingly difficult to maintain at the data sizes and query loads demanded by modern data systems. Therefore, it's more important than ever to design a system where these three layers can symbiotically exist with each other. We call this unified analytics.

In this tutorial, we’ll walk through a zero-migration solution for blazing-fast data lake analytics using StarRocks as the OLAP query layer. Also, we will see just how fast StarRocks is on the lake by comparing StarRocks and Presto in a TPC-DS 1TB test.


What To Know Before Getting Started

About StarRocks

First, what is StarRocks? If you're unfamiliar with this popular Linux project, you should know that StarRocks is a high-performance analytical database. It's designed to enable real-time, multi-dimensional, highly-concurrent data analysis across a wide variety of scenarios. You can get more details here, and download it for free here.


StarRocks' Hierarchy of the data object

From Catalog to Database to Table, StarRocks has a three-layered hierarchy of data objects.


StarRocks Catalog


  • The internal catalog contains internally managed databases and tables.

  • External catalog describes how to connect and authenticate to an external data system you want StarRocks to read data from.


Solution Architecture


StarRocks Solution Architecture


In this solution, we will use AWS as the infrastructure. We will:

  • Upload Parquet data to S3

  • Generate metadata in Amazon Glue

  • Deploy StarRocks with docker-compose

  • Prepare the necessary IAM role and policies to grant StarRocks access to S3 and Glue

Finally, we will use StarRocks' external catalog to retrieve Metadata in AWS Glue and query data in S3 directly without data load.


Solution Walkthrough

The SSB dataset

In this tutorial, we will use the SSB (Star Schema Benchmark) for demonstration. The SSB dataset is a standard benchmark dataset used for testing and evaluating the performance of database management systems (DBMS) and online analytical processing (OLAP) systems. On how to generate the SSB dataset or how to benchmark StarRocks with it, you can find more details on this page.

For demonstration purposes, in this exercise, we will use a modified version of SSB, which has the same schemas of the SSB dataset, but on a smaller scale at around 50MB in total size.


Upload the data into S3

The prepared 50MB SSB dataset can be downloaded from Then you can decompress and upload the entire ssb_50mb_parquet folder/directory into your desired S3 bucket. For detailed instructions on how to upload into S3, you can refer to the official AWS documentation. We recommend that you directly drag the whole directory into S3 using the Amazon S3 console.

For the remainder of the tutorial, let's assume that the dataset is in s3://<your-bucket>/ssb_50mb_parquet/.


AWS Glue

There are many ways to set up AWS Glue for the data on S3. In this tutorial, for simplicity, we will use crawler to do it.

First go to AWS Glue - Tables and click "Add tables using crawler"


AWS Glue Tables


For the data source, we simply input the S3 URL where the dataset is stored in. You can use the S3 URL of the ssb_50mb_parquet home directory, as the crawler is able to crawl all sub-directories recursively.


AWS Add Data Source


AWS Add Data Source Details


It is recommended to create new IAM role for AWS Glue Crawlers. However, you can use an existing role if one already exists.


AWS Configure Security Settings


Next... We create a database...


AWS Create Database


And we are done.


AWS Crawler Runs

AWS Database Properties


AWS authentications

In order to let StarRocks successfully access data in S3, we need to configure the following authentications:

  • Allow access to the S3 bucket

  • Allow access to Amazon Glue

In this tutorial, for simplicity, we will use the IAM instance profile to grant privileges to StarRocks.

Go to AWS IAM and create an IAM role with the following policies attached.

Note: If you are using an existing instance with instance profile attached, simply attach the following policies to the role.


AWS Select Trusted Entity


Regarding AWS IAM instance profile, you can read more in an AWS blog here.

For other methods such as IAM user or assumed role, you can read more here.



StarRocks accesses your S3 bucket based on the following IAM policy:

Note: Remember to change the <bucket_name> according to the bucket where you put the dataset in.


"Version": "2012-10-17",
"Statement": [
"Sid": "s3",
"Effect": "Allow",
"Action": ["s3:GetObject"],
"Resource": ["arn:aws:s3:::<bucket_name>/*"]
"Sid": "s3list",
"Effect": "Allow",
"Action": ["s3:ListBucket"],
"Resource": ["arn:aws:s3:::<bucket_name>"]



StarRocks accesses your AWS Glue Data Catalog based on the following IAM policy:

"Version": "2012-10-17",
"Statement": [
"Effect": "Allow",
"Action": [
"Resource": [


Deploy StarRocks

Launching an EC2 instance

We have a blog that covers StarRocks' deployment methods extensively. For this tutorial, if you want to get your hands dirty and follow along with the exercise, you can quickly spin up an EC2 instance and deploy a StarRocks environment with docker-compose.

When Launching the EC2 instance, in Advanced details - IAM instance profile, you can attach the role that you just created in the previous step.


EC2 Advanced Details


If you are using an existing EC2 instance, you can:

  • Attach the role to the EC2 instance (like the image below).

  • Attach the policy to the role that is already attached to the existing EC2 instance.


EC2 Modify



Deploy with docker-compose

With docker-compose, you can deploy a StarRocks environment in only a few simple steps.

Note that with this docker-compose yaml file, you will simulate a StarRocks Cluster with 1 FE and 1 BE in the docker environment.

Also, volumes are mounted for data persistence, all data including BE storage, FE metadata and logs for BE and FE are persisted in the ./starrocks directory when deployed.

Note: It is not recommended to deploy StarRocks in a production environment using docker-compose.


# Download the yaml template
# This template simulates a distributed StarRocks cluster with one FE and one BE

# Start the deployment with the yml file
sudo docker-compose up -d
# See the containers of FE and BEs
# See the simulated 1FE and 3BE cluster in the docker network
sudo docker ps
# Get the FE's IP
sudo docker inspect <fe-container-id> | grep IPAddress

# Connect to you StarRocks cluster using mysql client
mysql -h <fe-IPAddress> -P 9030 -uroot


Create an external catalog

We now have everything we need to create an external catalog to connect to AWS Glue. Because we previously setup using instance profile in the previous section, we only need to tell StarRocks to use instance profile, and we are good to go.

"type" = "hive",
"hive.metastore.type" = "glue",
"aws.glue.use_instance_profile" = "true",
"aws.s3.use_instance_profile" = "true",
"aws.glue.region" = "<glue_region>", -- e.g. us-west-2
"aws.s3.region" = "<s3_region>", -- e.g. us-west-2


Now we can take a look at what we have created:

-- To see the catalogs
show catalogs;

-- Show databases from the `lakehouse` catalog
show databases from lakehouse;

-- Use the database
use lakehouse.ssb_50mb_parquet;

show tables;


MySQL Results


StarRocks can now directly scan data on S3 without any data loading/migration.

MySQL [lakehouse.ssb_50mb_parquet]> select sum(lo_extendedprice*lo_discount) as revenue
from lineorder, dates
where lo_orderdate = d_datekey
and d_year = 1993
and lo_discount between 1 and 3
and lo_quantity < 25;


Revenue Results


Benchmark Results

One advantage of using StarRocks in data lake analytics is its performance. In this section, we take a look at just how fast a fully-fledged StarRocks cluster is on the lake.

The benchmark test is carried out on AWS, with StarRocks and Presto querying the same 1TB set of TPC-DS data on S3 in parquet format, and metadata in the Hive metastore on AWS EMR.






Presto is deployed with Hive together in AWS EMR.


EMR configuration

EMR Master
m5.xlarge * 1
EMR Core
r6i.2xlarge * 8


StarRocks configuration

StarRocks FE
m6.xlarge * 1
StarRocks BE
r6i.2xlarge * 8



Neither StarRocks nor Presto has local disk based cache turned on.



With the above configuration, StarRocks successfully completed all TPC-DS queries on 1TB, while Presto failed on query 95 with OOM.

Excluding query 95, StarRocks is 3.38 times faster than Presto.


StarRocks vs Presto Results


Get Started Unifying Your Data Lake Analytics Right Now

This tutorial highlights the seamless integration of StarRocks with the data lake ecosystem. By leveraging the external catalog feature, we showcased how StarRocks enables users to access the latest data in their data lake without the need for any data migration. Additionally, the performance comparison between StarRocks and Presto on the TPC-DS 1TB benchmark test revealed that StarRocks outperforms Presto by 3.38 times. These benefits make StarRocks a powerful tool for businesses looking to efficiently manage and analyze their data at scale.


You can try this for yourself right now. Download StarRocks here to get started, join the community Slack channel, and check out the StarRocks documentation page to learn more.



Detailed TPC-DS 1TB Benchmark results:

TPC-DS query StarRocks (ms) Presto (ms)
QUERY01 1396 5602
QUERY02 7564 67612
QUERY03 4453 19257
QUERY04 83890 179031
QUERY05 11471 116819
QUERY06 4324 75175
QUERY07 9503 38054
QUERY08 4435 18571
QUERY09 33782 24845
QUERY10 4720 24162
QUERY11 55866 96272
QUERY12 2166 8071
QUERY13 14327 48399
QUERY14-1 29239 136633
QUERY14-2 31269 145513
QUERY15 3106 10042
QUERY16 7369 31600
QUERY17 9274 39211
QUERY18 4972 17163
QUERY19 7593 25529
QUERY20 2204 14219
QUERY21 2354 12873
QUERY22 11825 307246
QUERY23-1 47558 201035
QUERY23-2 47491 206666
QUERY24-1 9038 76511
QUERY24-2 8835 76487
QUERY25 10985 42687
QUERY26 3556 18346
QUERY27 9170 36419
QUERY28 26926 102762
QUERY29 10649 48294
QUERY30 2172 5201
QUERY31 8279 67779
QUERY32 3154 21816
QUERY33 8459 31885
QUERY34 6128 24672
QUERY35 5647 25013
QUERY36 7393 27418
QUERY37 3049 17389
QUERY38 19992 39870
QUERY39-1 2205 13207
QUERY39-2 2016 12378
QUERY40 2643 21859
QUERY41 224 702
QUERY42 4316 18426
QUERY43 5451 16222
QUERY44 15649 48599
QUERY45 2524 7057
QUERY46 10769 39547
QUERY47 10472 57071
QUERY48 9075 31896
QUERY49 13434 57325
QUERY50 12877 44546
QUERY51 9396 22463
QUERY52 4213 18288
QUERY53 4810 33088
QUERY54 6759 58635
QUERY55 4241 18279
QUERY56 8498 37189
QUERY57 4612 35054
QUERY58 6620 50588
QUERY59 14672 32286
QUERY60 8384 33503
QUERY61 14968 65473
QUERY62 2020 6520
QUERY63 4938 32817
QUERY64 18243 237344
QUERY65 13701 37700
QUERY66 7722 28130
QUERY67 83858 22395
QUERY68 11868 45134
QUERY69 5441 22879
QUERY70 13581 36521
QUERY71 9895 37342
QUERY72 163834 43341
QUERY73 6084 25284
QUERY74 33158 102358
QUERY75 17449 172819
QUERY76 8789 29867
QUERY77 9334 44491
QUERY78 29065 77308
QUERY79 11439 38682
QUERY80 15420 73728
QUERY81 1811 6300
QUERY82 4338 27938
QUERY83 960 5371
QUERY84 981 3606
QUERY85 2288 17002
QUERY86 2195 6448
QUERY87 19935 58678
QUERY88 29254 17461
QUERY89 9428 33007
QUERY90 3003 5255
QUERY91 637 2477
QUERY92 7198 12313
QUERY93 34985 155395
QUERY94 9907 15563
QUERY95 39820 OOM
QUERY96 3395 17456
QUERY97 14429 27018
QUERY98 14461 29098
QUERY99 3931 12412


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