Category: TPC-H

Benchmarking , Snowflake, Databricks , Synapse , BigQuery, Redshift , Trino , DuckDB and Hyper using TPCH-SF100

(Disclaimer : I use BigQuery for a personal project and it is virtually free for smaller workload, at work we use SQL Server as a Data Store, I will try my best to be objective )

TL:DR ;

Run TPCH-SF100 benchmark (base table 600 million rows) to understand how different Engine Behave in this workload using just the lowest tier offering, you can download the results here

Introduction

Was playing with Snowflake free trial ( maybe for the fifth time) and for no apparent reason, I just run Queries on TPCH-S100 Dataset, usually I am interested in smaller dataset, but I thought how Snowflake may behave with bigger data using the smallest cluster, long story short, I got 102 second, posted it in Linkedin and a common reaction was Snowflake is somehow cheating.

Obviously I did not buy the cheating explanation , as it is too risky and Databricks will make it international news.

Load the Data Again

Ideally I would have generated the Data myself and load it into Snowflake, generating 600 Million records in my laptop is not trivial, my tool of choice, DuckDB has an utility for that but it is currently single threaded, instead

  • I exported the data from Snowflake to Azure Storage as parquet files
  • Download it to my Laptop, generate new files using DuckDB as in Snowflake you can’t control the minimum size of files, you can control the max but not the Min

Snowflake Parquet External Table

My Plan was to run Queries directly on Parquet hosted on azure storage, the experience was not great at all, Snowflake got Query 5 join order wrong

Snowflake Internal Table

I loaded the parquet files generated by duckdb, Snowflake getting extremely good results. what I learnt, whatever Snowflake magic is doing, it is related to their proprietary file format.

BigQuery External Table

I have no frame of reference for this kind of workload, so I loaded the the data to BigQuery using external table in Google Cloud, Google got 5 minutes, one Run, 2.5 $ !!!!

BigQuery Internal Table

Loaded Data to BigQuery internal format, notice, BigQuery don’t charge for this operation , 2 Minutes 16 second, 1 Cold Run.

BigQuery Standard Edition

BigQuery added new pricing model where you pay by second, after the first minutes, I used the Standard Edition with a small size, I run the same query two time, unfortunately the new distributed disk cache don’t seems to be working, same result 5 minutes, that’s was disappointing

Redshift Serverless

Imported the same Parquet files into Redshift serverless, The schema was defined without Distribution keys, The results are for 3 Runs, the first run was a bit slower as it is fetching the data from the managed storage to the compute SSD the other 2 runs are substantially faster, I thought it is fair to have an average, Using the lowest Tier 8 RPU (2.88 $/Hour)

Redshift Serverless hot run was maybe the fastest performance I have seen so far, but they need still to improve on their cold Run.

I was surprised by the system overall performance, from my reading, it seems AWS basically rewrite the whole thing including separating compute from storage, Overall I think it is a good DWH.

Trino

Trino did not run Query 15, had to run a modified syntax but same results, 1 Run from Cold Storage, I am using the excellent service from Starburst Data

Synapse Serverless

Honestly, I was quite surprised by the performance of synapse serverless, initially I tested with the smaller file size generated by Snowflake and it did work, the first run failed but the second works just fine, I did like it, it did failed quickly, notice that Synapse run statistics on parquet files, so you would expect a more stable performance, not the fastest, but rather resilient.

Anyway , it took from 8-11 minutes, to be clear that’s not Synapse from two years ago.

Not related to the benchmark but I did enjoyed the lake database experience

Databricks External Table

I had not a great experience with Databricks, I could not simply pass authentication to Databricks SQL, you need a service principal and registering an App, and the documentation keep talking about Unity, which is not installed by default, This is a new install why Unity is not embedded if it is such a big deal ?

Anyway, First I created an external Table in databricks using the excellent passthrough technique in the Single Node Cluster, Databricks got 12 minutes,

Databricks Delta table

let’s try again with Delta, I created a new managed table, run optimize and analyse , (I always thought delta has already the stats), but it didn’t seems to make a big difference, still around 11 minutes, and this running from the disk, so no network bottleneck

DuckDB

My Plan was to run DuckDB on Azure ML, but I need a bigger VM than the one provided by default, I could not find a way to increase my Quota , I know it sounds silly, and I am just relating my experience, turn out Azure ML VM Quota is different from Azure VM, it did drive me crazy why I could get any VM in Databricks but Azure ML keep complaining I don’t have enough CPU.

Unfortunately I hit two bugs, first the native DuckDB file format seems to generate double the size of Parquet, the dev was very quick to identify the issue, the workaround is to define the table schema and then load the data using insert, the file became 24 GB compared to the original 40GB parquet files.

I End Up going with parquet files, I was not really excited by loading a 24 GB file in a storage account.

I run the Queries in Azure Databricks VM E8ds_v4 (8 cores and 64 GM of RAM)

As I am using fsspec with disk cache, the remote storage is used only the first run, after 4 tries, Query 21 keep crashing the VM 😦

Tableau Hyper

Tableau hyper was one of the biggest surprise, unfortunately, I hit a bug with Query 18, otherwise, it would have being the cheapest option.

Some Observations

  • Initially I was worried I made a mistake in Snowflake results, the numbers are just impressive for a single node tier, one explanation is the Execution Engine is mostly operating on compressed data with little materialization , but whatever they are doing, it has to do with the internal table format, which bring a whole discussion of performance vs openness, personally in a BI scenarios, I want the best performance possible, and wonder if they can get the same speed using Apache Iceberg.
  • Synapse Serverless improved a lot from last year, it did work well regardless of the data size of individual parquet files that I throw at it, and in my short testing it was faster than databricks and you pay by data scanned, so strictly speaking pure speed is not such a big deal but without a free result cache like BigQuery, it is still a hard sell.
  • Azure ML Quota policy was very confusing to me, and honestly I don’t want to deal with support ticket.
  • Databricks; may well be the fastest to run 100 TB, but for 100 GB workload, color me unimpressed.
  • DuckDB is impressive for an open source project that did not even reach version 1. I am sure those issues will be fixed soon.
  • Everything I heard about Redshift from twitter was wrong, it is a very good DWH, with Excellent performance.
  • BigQuery as I expected has excellent performance both for parquet and the native table format, The challenge is to keep the same using the new auto scale offering. added Auto scale performance, I think Google should do better.

Summary Results

You can find the results here, if you are a vendor and you don’t like the results feel free to host a TPCH-SF100 dataset in your service and let people test it themselves.

Note : Using SQL Query History : Bigquery one Cold Run , Synapse Serverless , Redshift Serverless and Snowflake a mix of cold and warm

(Note : Synapse Serverless always read from remote storage)

Databricks I am showing the best run from Disk, there is no system table, so I had to copy paste the results from the console.

Pricing

I did not kept the durations for Data load, it is just the cost for Read, obviously it is a theoretical exercise, and does not reflect real life usage which depends on other factors like concurrency performance , how you can share a pool of resources to multiple departement,free results cache, the performance of your ODBC drivers etc.

it is extremely important to understand what’s included in the basic price, for example.

Results cache:

BigQuery, Snowflake, Redshift results cache are free and you don’t need a running cluster, in Databricks you pay for it, Synapse don’t offer result cache at all.

Data loading :

BigQuery data loading is a free operation and other service like sorting and partitioning, in other DB you needs to pay.

Egress Fees :

Snowflake/BigQuery offer free egress fees, Other vendors you may pay, you need to check

Note :

BigQuery : for This workload make more sense to pay by compute not data scanned, either using auto scale, reserved pricing etc, I will try to test Auto scaling later.

Snowflake : I used the standard edition of Snowflake

Edit : I used a Google Colab notebook with a bigger VM for Hyper and DuckDB, see full reproducible notebook

Final Thoughts

Cloud DWH are amazing tech and only competition can drive innovation, not FUD and dishonesty, regardless of what platform you use, keep an eye on what other vendors are doing, and test using your own workload, you may be surprised by what you find.

Benchmarking Synapse Serverless using TPC-H-SF10

Edit : February 2023 , Synapse serverless has substantially improved the Query performance for TPCH-SF10, first run which include calculating the statistics take around 2 minutes, but the second run is around 62 second.

In a previous blog, I did a benchmark for a couple of Database Engine, although it was not a rigorous test, pretty much the results were in the expected range, except for Synapse serverless, I got some weird results, and not sure if it is by design or I am doing something very wrong, so I thought it worth showing the steps I took hoping to find what’s exactly going on. The test was done in January 2022.

First Check : Same region

I am using an azure storage in Southeast Asia

My synapse Instance is in the same region

Ok both are in the same region, first best practice.

Loading Data into Azure Data Store

The 8 parquet files are saved in this Google drive, so anyone can download it,

Define Schema

In Synapse, you can directly start querying a file without defining anything, using Openrowset, I thought I can test TPC-H Query 1 as it uses only 1 table, which did not work , some kind of case sensitive issue, when writing this blog I run the same Query and it worked just fine, ( no idea what changed)

1 minute on a second run, hmm not good, let’s try a proper external table , the data_source and File_format were already defined, so need to recreate it again.

CREATE EXTERNAL TABLE lineitem_temp (
	[L_ORDERKEY] bigint,
	[L_PARTKEY] bigint,
	[L_SUPPKEY] bigint,
	[L_LINENUMBER] bigint,
	[L_QUANTITY] float,
	[L_EXTENDEDPRICE] float,
	[L_DISCOUNT] float,
	[L_TAX] float,
	[L_RETURNFLAG] nvarchar(1),
	[L_LINESTATUS] nvarchar(1),
	[L_SHIPINSTRUCT] nvarchar(25),
	[L_SHIPMODE] nvarchar(10),
	[L_COMMENT] nvarchar(44),
	[l_shipdate] datetime2(7),
	[l_commitdate] datetime2(7),
	[l_receiptdate] datetime2(7)
	)
	WITH (
	LOCATION = 'lineitem.parquet',
	DATA_SOURCE = [xxx_core_windows_net],
	FILE_FORMAT = [SynapseParquetFormat]
	)
GO


SELECT count (*) FROM dbo.lineitem_temp
GO

A Proper Table with Data type and all

let’s try again the same Query 1

ok 2 minute for the first run, let’s try another run which will use statistics, it should be faster, 56 second ( btw, you pay for those statistics too)

Not happy with the results I asked Andy ( Our Synapse expert) and he was kind enough to download and test it, he suggested splitting the file give better performance , he got 16 second.

CETAS to the rescue

Create External Table as Select is a very powerful functionality in Serverless, The code is straightforward

CREATE EXTERNAL TABLE lineitem 
	WITH (
	LOCATION = '/lineitem',
	DATA_SOURCE = [xxxx_core_windows_net],
	FILE_FORMAT = [SynapseParquetFormat]
	)
as
SELECT * FROM dbo.lineitem_temp

Synapse will create a new table Lineitem with the same data type and a folder that contain multiple parquet files.

That’s all what you can do, you can’t partition the table, you can’t sort the table, but what’s really annoying you can’t delete the table, you have first to delete the table from the database then delete the folder

but at least it is well documented

Anyway, let’s see the result now

Not bad at all, 10 second and only 587 MB scanned compared to 50 second and 1.2 GB.

Now that I know that CETAS has better performance, I have done the same for remaning 7 tables.

Define all the tables

First Create an external Table to define the type then a CETAS, Synapse has done a great job guessing the type, I know it is parquet after all, but varchart is annoying by default it is 4000, you have to manually adjust the correct length.

TPC-H document contains the exact schema

Running the Test

The 22 Queries are saved here, I had to do some change to the SQL, changing limit to Top and extract year from x to Year (x), Query 15 did not run, I asked the Question on Stackoverflow and Wbob kindly answer it very quickly

The first run, I find some unexpected results

I thought I was doing something terribly wrong, the Query duration seems to increase substantially, after that I start messing around, what I found is, if you run just one Query at the time, or even 4, the results are fine, more than that, and the performance deteriorate quickly.

A Microsoft employee was very helpful and provided this script to Query the Database History

I imported the Query History to PowerBI and here is the results

There is no clear indication in the documentation that there is a very strict concurrency limitation, I tried to run the Script in SSMS and it is the same behavior, that seems to me the Engine is adding the Queries to a queue, there is a bottleneck somewhere.

Synapse serverless show the duration between when the Job was submitted until it is completed, there is no way to know the actual Duration of each Query, so basically the Overall Duration is the duration of the Last Query, in our Case Q22, which is around 3 Minutes.

Takeway

The Good news, the product team made it very clear, Synapse Serverless is not an Interactive Query Engine

Realistically speaking, reading from Azure storage will always be slower compared to a local SSD Storage, so no I am not comparing it to other DWH offering, having said that even for exploring files on azure storage, the performance is very problematic.

Benchmark Snowflake, BigQuery, SingleStore , Databricks, Datamart and DuckDB using TPC-H-SF10

Edit 18 May 2022: Microsoft released Datamart which has excellent performance for this type of Workload.

Another blog on my favorite topic, interactive Live BI Workload with low latency and high concurrency, but this time, hopefully with numbers to compare.

I tested only the Databases that I am familiar with, BigQuery, Snowflake, Databricks , SingleStore , PowerBI Datamart and DuckDB

TPC-H

The most widely used Benchmark to test BI workload is TPC-DS and TPC-H produced by the independent Organization TPC, unfortunately most of the available benchmark are for big dataset starting from 1 TB, as I said before I more interested in smaller Workload for a simple reason, after nearly 5 years of doing Business intelligence for different companies, most of the data model are really small, ( my biggest was 70 Million rows with 4 small dimension tables).

Benchmarking is a very complex process, and I am not claiming that my results are correct, all I wanted to know as a user is an order of magnitude and a benchmark can give you a high level impression of a database performance.

Schema

I Like TPC-H as it has a simpler schema 8 tables and only 22 Queries compared to TPC-DS which require 99 Queries.

Image

Some Considerations

  • Result Cache is not counted.
  • The results are using warm cache and at least one cold run, I run the 22 Queries multiple times.
  • Databricks by default provide a sample Database TPC-SF05, the main Table Lineitem is 30 Millions rows, I don’t know enough to import the data and apply the proper sorting etc , so I preferred to use the smaller dataset. I did create a local copy by using create table as select ( Loaded SF10 Data)
  • Snowflake and SingleStore have SF10 and other scale by default.
  • BigQuery, I imported the data from Snowflake , I sorted the tables for better performance, it is a bit odd that BigQuery don’t provide such an important public dataset by default
  • Microsoft Datamart no sorting or partitioned was applied , the data was imported from Biguery.

No Results Cache

Most DWH support results cache, basically if you run the same Query and the base tables did not change the Engine will return the same results very quickly, obviously in any benchmark, you need to filter out those queries.

  • In Snowflake you can use this statement to turn the results cache off 
ALTER SESSION SET USE_CACHED_RESULT = FALSE;
  • In Databrick
SET use_cached_result = false
  • BigQuery, just add an option in the UI
  • SingleStore and Datamart, does not have a result cache per se, the engine just keep a copy of the Query Plan, but it scan The Data every time.

Warm Cache

Snowflake, SingleStore and Databricks leverage the local SSD cache, when you run a Query the first time, it scan the data from the cloud storage which is a slow operation, then when you run it again the Query will try to use the data already copied in the local disk which is substantially faster, specially with Snowflake if you want to keep the local warm cache it make sense to keep your Cluster running a bit longer.

BigQuery is a different beast there is no VM, the data is read straight from the Google Cloud Storage, yes google Cloud Network is famous for being very fast, but I guess it can not compete with a local SSD Disk, anyway that’s why we have BI Engine which basically cache the data in RAM, but not all Queries are supported, actually only 6 are fully accelerated as of this writing. ( see Limitations )

Query History

Getting Query results is very straightforward using information_Schema, except for databricks, it seems it is only supported using an API, I just copied one warm run and paste it to excel and load it from there.

Engine Used

  • Snowflake : X-Small (Lowest tier)
  • Databricks : 2X-Small (Lowest tier)
  • Single Store : S-0
  • BigQuery : on Demand + 1 GB Reservation of BI Engine
  • Datamart : included with PowerBI Premium, official spec not disclosed.
  • DuckDB : my laptop, 16GB RAM 🙂

Results

The 22 Queries are saved in this repo, I am using PowerBI to combine all the results

let’s start with

Snowflake VS BigQuery

Snowflake Vs SingleStore

Snowflakes VS Databricks

Notice Databricks is using the smaller Dataset SF05, 30 million rows and still Snowflake show better performance

Overall

Edit : due to feedback, I am adding the sum of all Queries, You can download the results here

Edit : 26-Jan-2022, I Updated the results for Databricks SF10, I Uploaded the same data used for BigQuery, then created Delta Table and applied optimize Z Order

Take away

  • Snowflake is very fast and has consistent results for all the 22 Queries, Except Query 13 is a bit odd

  • SingleStore is remarkable but Query 13 is not good at all and skew the overall performance.

  • BigQuery is fantastic when BI Engine works ( only 11 Queries are supported from the total of 22)

  • Databricks performance in TPC-H-SF05 is problematic, I just hope they release a proper TPC-H-SF10 dataset and information schema like other DWH

  • Datamart has the best user experience, the only Data Platform where you can load the data without writing any Code,The same as Singlestore; Query 13 has a very Big Cost on the overall performance.

  • DuckDB : Query 9 skew the overall performance and probably I need a new laptop 🙂

First Look at SingleStore

I could have written a nice paragraph why I got interested in SingleStore but to be honest the reason is very simple and has nothing about the tech, Jordan Tigani one of the founding Engineers of BigQuery is now their Chief Product Officer, so I became very curious 🙂

Again, I am only interested in Small Interactive BI Workload, contrary to the usual Suspects ( BigQuery, Snowflake etc), SingleStore is not a pure Data warehouse but rather a multi purpose database, it does OLTP Workloads but has an excellent support for OLAP Workload, I am only interested in Analytical Workloads.

Setup

There is a free trial with $500, the setup was very intuitive, I really liked the way you create a new Cluster, notice I don’t have an account with AWS, but it is a software as a service Experience, SingleStore manage everything on behalf of the user, I chose AWS as they support the Sydney Region.

The smallest tier start at 0.25 Credit/hour, which cost 0.65 $/Hour, Unlike Snowlake and Databricks there is no auto suspend and auto start, you have to do it manually.

For some reason Suspend a Cluster is not available in Google Cloud !!!

The Console has the bare minimum but functional, there is no multiple tab, if you run a Query, you need to wait till it is done before running another one

There is an odd choice in the UI, when you want to monitor the Cluster you need to open a new page called SingleStore Studio

It is not the end of the world, but a bit annoying when you are new to the product

Loading Data

There is sample Data you can quickly load to start running Queries, but I wanted to test only my own dataset (TPC-H- SF10) ( nice surprise it was added this week)

Although my Cluster is in AWS, loading files from Google Cloud was trivial, all I had to do is setup a new pipeline

first define the table, notice the Clustered Columnstore key

CREATE TABLE `orders` (
`o_orderkey` bigint(11) NOT NULL,
`o_custkey` int(11) NOT NULL,
`o_orderstatus` char(1) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL,
`o_totalprice` decimal(15,2) NOT NULL,
`o_orderdate` date NOT NULL,
`o_orderpriority` char(15) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL,
`o_clerk` char(15) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL,
`o_shippriority` int(11) NOT NULL,
`o_comment` varchar(79) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL,
SHARD KEY (`o_orderkey`) USING CLUSTERED COLUMNSTORE
);

Define a new pipeline

CREATE or REPLACE PIPELINE `LoadGCPorders`
AS LOAD DATA GCS 'xxxxxxxxxxxxx'
CREDENTIALS '{"access_id": "xxx", "secret_key": "xxxxxx"}'
INTO TABLE tpch.orders
(`O_ORDERKEY` <- `O_ORDERKEY`,
  `O_CUSTKEY` <- `O_CUSTKEY`,
  `O_ORDERSTATUS` <- `O_ORDERSTATUS`,
  O_TOTALPRICE <- O_TOTALPRICE
  , O_ORDERDATE <- O_ORDERDATE
  , O_ORDERPRIORITY <- O_ORDERPRIORITY
  , O_CLERK <- O_CLERK
  , O_SHIPPRIORITY <- O_SHIPPRIORITY
  , O_COMMENT <- O_COMMENT
  
  )
FORMAT PARQUET

Then Run the pipeline and the data is automatically loaded, very nice

START PIPELINE LoadGCPorders FOREGROUND;

Testing using TPC-H SF10 Benchmark

to test all the 22 Queries of the benchmark I used the same script for BI Engine, here is the results after 10 runs using the Dataset Provided by Singlestore ( Using S-0 Cluster, see pricing here)

A lot of Queries are already under a second even when using a lower tier !!! Queries 13 result is a bit odd.

if I understood correctly, SingleStore does not have a results cache when you run the same Query again, SingleStore store the Query plan but scan the data again, although the data is stored on Disk, metadata on the tables is stored In-Memory ( tables for OLTP workload are always In-Memory)

The Previous chart was built using Google Data Studio, as of this writing, PowerBI does not have a native connector, you need to download a custom connector which means you need a gateway, not sure if Direct Query is supported at all, I quickly used MySQL connector which works fine but import mode only (SingleStore is compatible with MySQL tools)

Take Away

I was really impressed by the Product, we all hear about this operational analytics and it seems SingleStore has a good solution, there are missing functionalities though, Auto suspend and resume is not available yet and no native connector for PowerBI is very problematic, but it is really Fast and do write workload too.