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.

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