Supercharge your Analytics with ClickHouse, v.2. By Vadim Tkachenko

Oct 26, 20175 likes1,013 views

This document summarizes and compares different database options. It discusses how ClickHouse is fast and free as an open source option, while commercial options are faster but more expensive. It then provides details on how ClickHouse achieves high performance through vectorized processing and parallel query execution across servers.

Supercharge your Analytics with ClickHouse, v.2. By Vadim Tkachenko

Commercial
Fast and
Expensive
ClickHouse
Fast and Free
OpenSource
Free, but…

Data is represented as small single-dimensional arrays (vectors), easily accessible for CPUs.
The percentage of instructions spent in interpretation logic is reduced by a factor equal to the
vector-size
The functions that perform work now typically process an array of values in a tight loop
Tight loops can be optimized well by compilers, enable compilers to generate SIMD instructions
automatically.
Modern CPUs also do well on such loops, out-of-order execution in CPUs often takes multiple loop
iterations into execution concurrently, exploiting the deeply pipelined resources of modern CPUs.
It was shown that vectorized execution can improve data-intensive (OLAP) queries by a factor 50.

X
Y
*
Scalar
operation
X1
Y1
*
SIMD
(Single instruction, multiple data)
X2
Y2
*
X3
Y3
*
Xn
Yn
*

Referer CounterID Date Username Country
Read only needed columns
Compression,Vectorizedprocessing
SELECT Referer, count(*) AS count
FROM hits
WHERE CounterID = 1234 AND Date >=
today() - 7
GROUP BY Referer
ORDER BY count DESC LIMIT 10
Discard blocks by index
(CounterID, Date)
Parallel processing

Query 1. Speedup vs CPU Threads
CPU Threads

Query 3. Speedup vs CPU Threads
CPU Threads

SELECT foo FROM distributed_table
SELECT foo FROM local_table GROUP BY col1
• Server 1
SELECT foo FROM local_table GROUP BY col1
• Server 2
SELECT foo FROM local_table GROUP BY col1
• Server 3

N Servers 1 3 140
Time, sec 1.224 0.438 0.043
Speedup x2.8 x28.5

• Facts
• Dimensions
• Metrics
• Projections

ClickHouse on MemCloud
Kodiak Data and Altinity now Offer a Cloud Version of ClickHouse
45
1. FASTEST MPP Open Source DBMS
2. Cutting Edge Cloud for Big Data Apps and Processing
3. World-class ClickHouse Expertise
Try the ClickHouse on MemCloud demo here
http://clickhouse-demo.memcloud.works/

The document outlines an agenda for a ClickHouse event, including presentations on ClickHouse features and usage. ClickHouse is introduced as a fast, flexible, and free open source columnar database management system. It is described as being very fast for analytical queries, with the ability to ingest and aggregate data at low latency and perform distributed computations and fault-tolerant data warehousing at large scale. Successful production deployments are listed in domains like analytics, advertising, operations, and security. The growth and adoption of ClickHouse is discussed.

Migration to ClickHouse. Practical guide, by Alexander ZaitsevAltinity Ltd

This document provides a summary of migrating to ClickHouse for analytics use cases. It discusses the author's background and company's requirements, including ingesting 10 billion events per day and retaining data for 3 months. It evaluates ClickHouse limitations and provides recommendations on schema design, data ingestion, sharding, and SQL. Example queries demonstrate ClickHouse performance on large datasets. The document outlines the company's migration timeline and challenges addressed. It concludes with potential future integrations between ClickHouse and MySQL.

Webinar 2017. Supercharge your analytics with ClickHouse. Vadim TkachenkoAltinity Ltd

ClickHouse is an open-source column-oriented database management system designed for high speed analytics on large data sets. It was developed by Yandex to handle real-time reporting and analytics for their search engine and ad analytics platforms, processing billions of events daily. ClickHouse provides massively parallel processing, distributed query processing, and high reliability. Benchmarks show it outperforms other open-source and commercial analytics databases in speed. It is best suited for analytics workloads like web and app analytics, sensor data, and monitoring where fast queries and scalability to large data volumes are important.

ClickHouse 2018. How to stop waiting for your queries to complete and start ...Altinity Ltd

ClickHouse Paris Meetup. Pragma Analytics Software Suite w/ClickHouse, by Mat...Altinity Ltd

Pragma Innovation is an IT services company focused on time series data solutions. Their PASS (Pragma Analytics Software Suite) allows companies to analyze, report on, and make decisions from time series network data using open source software. It is designed for ISPs, hosting providers, and telecom companies. The solution ingests network and log data, standardizes it, enriches it using tools like GeoIP, and stores it in a time series database. This allows customers to build applications for traffic engineering, security, and business intelligence use cases. Key challenges addressed in version 2.0 of the solution include data sampling, IPv4/IPv6 support, and using ClickHouse as the time series database for its performance and simplicity

ClickHouse in Real Life. Case Studies and Best Practices, by Alexander ZaitsevAltinity Ltd

ClickHouse Data Warehouse 101: The First Billion Rows, by Alexander Zaitsev a...Altinity Ltd

Columnar stores like ClickHouse enable users to pull insights from big data in seconds, but only if you set things up correctly. This talk will walk through how to implement a data warehouse that contains 1.3 billion rows using the famous NY Yellow Cab ride data. We'll start with basic data implementation including clustering and table definitions, then show how to load efficiently. Next, we'll discuss important features like dictionaries and materialized views, and how they improve query efficiency. We'll end by demonstrating typical queries to illustrate the kind of inferences you can draw rapidly from a well-designed data warehouse. It should be enough to get you started--the next billion rows is up to you!

Adventures in Observability: How in-house ClickHouse deployment enabled Inst...Altinity Ltd

This document discusses Instana's use of ClickHouse as a data store for application monitoring data. It provides an overview of Instana's APM capabilities, how it collects and analyzes data using ClickHouse, and lessons learned from operating ClickHouse clusters at scale. Key points include that Instana uses ClickHouse to store trace data and generate dashboards, ClickHouse helps process millions of records quickly, and Instana monitors ClickHouse using its own APM platform to gain end-to-end visibility of performance.

ClickHouse Introduction by Alexander Zaitsev, Altinity CTOAltinity Ltd

This document summarizes a ClickHouse meetup agenda. The meetup included an opening by Javier Santana, an introduction to ClickHouse by Alexander Zaitsev of Altinity, a presentation on 2019 new ClickHouse features by Alexey Milovidov of Yandex, a coffee break, a presentation from Idealista on migrating from a legacy system to ClickHouse, a presentation from Corunet on analyzing 1027 predictive models in 10 seconds using ClickHouse, a presentation from Adjust on shipping data from Postgres to ClickHouse, closing remarks, and a networking session. The document then provides an overview of what ClickHouse is, how fast it can be, how flexible it is in deployment options, how

Webinar 2017. Supercharge your analytics with ClickHouse. Alexander ZaitsevAltinity Ltd

Alexander Zaitsev presented on LifeStreet's experience implementing ClickHouse for their ad analytics platform. Some key points: - LifeStreet processes over 10 billion events per day from their ad exchange and needed a high performance analytics solution. - They tried various databases but migrated fully to ClickHouse due to its performance for analytics workloads. - Major challenges included designing an efficient schema, sharding and replication strategy, and reliable data ingestion. - ClickHouse's dictionary feature allowed them to implement normalized dimensions tables while supporting updates, improving storage efficiency and query performance.

ClickHouse Introduction, by Alexander Zaitsev, Altinity CTOAltinity Ltd

The document outlines an agenda for a ClickHouse conference in Paris on October 3rd 2019. The agenda includes an introduction to ClickHouse by Alexander Zaitsev of Altinity, a presentation from Christophe Kalenzaga and Vianney Foucault of ContentSquare on migrating from ElasticSearch to ClickHouse, and several other talks on using ClickHouse for applications such as mobile analytics, operational logs analysis, and an overview of new ClickHouse features.

Building an Observability platform with ClickHouseAltinity Ltd

The document discusses using ClickHouse as the backend storage for observability data in SigNoz, an open source observability platform. ClickHouse is well-suited for storing observability data due to its ability to handle wide tables, perform fast aggregation queries, and provide compression capabilities. The document outlines SigNoz's architecture, demonstrates how tracing and metrics data is modeled in ClickHouse, and shows how ClickHouse outperforms Elasticsearch for ingesting and querying logs and metrics at scale. Overall, ClickHouse is presented as an efficient and less resource-intensive solution than alternatives like Druid for storing observability data, especially for open source projects.

Clickhouse at Cloudflare. By Marek VavrusaValery Tkachenko

Cloudflare uses ClickHouse to analyze over 1 million DNS queries per second from its global network. ClickHouse is a column-oriented database that allows Cloudflare to perform complex ad-hoc queries and aggregations over trillions of rows of DNS log data with dimensions like timestamp, zone, and location. They store raw logs for 3 months and aggregated data indefinitely to monitor trends and traffic over time. The multi-tenant ClickHouse cluster at Cloudflare inserts over 8 million rows per second and has excellent query performance for common aggregations used in their analytics.

Dangerous on ClickHouse in 30 minutes, by Robert Hodges, Altinity CEOAltinity Ltd

- The document summarizes a presentation about ClickHouse, an open source column-oriented database management system. - It discusses how ClickHouse stores and indexes data to enable fast queries, how it scales horizontally across servers, and how different engines like MergeTree and ReplicatedMergeTree allow for high performance and fault tolerance. - Examples are provided showing how ClickHouse can quickly analyze large datasets with SQL and optimize queries using its features like distributed processing, partitioning, and specialized functions.

ClickHouse Deep Dive, by Aleksei MilovidovAltinity Ltd

This document provides an overview of ClickHouse, an open source column-oriented database management system. It discusses ClickHouse's ability to handle high volumes of event data in real-time, its use of the MergeTree storage engine to sort and merge data efficiently, and how it scales through sharding and distributed tables. The document also covers replication using the ReplicatedMergeTree engine to provide high availability and fault tolerance.

Clickhouse Capacity Planning for OLAP Workloads, Mik Kocikowski of CloudFlareAltinity Ltd

High Performance, High Reliability Data Loading on ClickHouseAltinity Ltd

This document provides a summary of best practices for high reliability data loading in ClickHouse. It discusses ClickHouse's ingestion pipeline and strategies for improving performance and reliability of inserts. Some key points include using larger block sizes for inserts, avoiding overly frequent or compressed inserts, optimizing partitioning and sharding, and techniques like buffer tables and compact parts. The document also covers ways to make inserts atomic and handle deduplication of records through block-level and logical approaches.

HTTP Analytics for 6M requests per second using ClickHouse, by Alexander Boc...Altinity Ltd

This document summarizes Cloudflare's use of ClickHouse to analyze over 6 million HTTP requests per second. Some key points: - Cloudflare previously used PostgreSQL, Citus, and Flink but these did not scale sufficiently. - ClickHouse was chosen as it is fast, scalable, fault tolerant, and Cloudflare had existing expertise in it. - Cloudflare designed ClickHouse schemas to aggregate HTTP data into totals, breakdowns by category, and unique counts into two tables using different engines. - Tuning ClickHouse index granularity improved query latency by 50% and throughput by 3x. - The new ClickHouse pipeline is more scalable, fault tolerant

ClickHouse Analytical DBMS: Introduction and Case Studies, by Alexander ZaitsevAltinity Ltd

A Fast Intro to Fast Query with ClickHouse, by Robert HodgesAltinity Ltd

Slides for the Webinar, presented on March 6, 2019 For the webinar video visit https://www.altinity.com/ Extracting business insight from massive pools of machine-generated data is the central analytic problem of the digital era. ClickHouse data warehouse addresses it with sub-second SQL query response on petabyte-scale data sets. In this talk we'll discuss the features that make ClickHouse increasingly popular, show you how to install it, and teach you enough about how ClickHouse works so you can try it out on real problems of your own. We'll have cool demos (of course) and gladly answer your questions at the end. Speaker Bio: Robert Hodges is CEO of Altinity, which offers enterprise support for ClickHouse. He has over three decades of experience in data management spanning 20 different DBMS types. ClickHouse is his current favorite. ;)

Clickhouse MeetUp@ContentSquare - ContentSquare's Experience SharingVianney FOUCAULT

ClickHouse on Plug-n-Play Cloud, by Som Sikdar, Kodiak DataAltinity Ltd

Kodiak provides a private cloud solution called MemCloud that offers faster performance and lower costs compared to public clouds like AWS. MemCloud can be deployed on-premises or at the edge to power analytics, big data, and AI/ML workloads. Benchmarks show the Kodiak solution is up to 5x faster than AWS for similar configurations. It also reduces the complexity, costs, and maintenance challenges of building and tuning physical data lake clusters that combine different software like HDFS, Kafka, Spark and ClickHouse.

Bitquery GraphQL for Analytics on ClickHouseAltinity Ltd

Data Warehouse on Kubernetes: lessons from Clickhouse OperatorAltinity Ltd

1) Kubernetes operators can simplify deploying data warehouses like ClickHouse from a single specification and provide portability, fast deployment, and flexible resource management. 2) While the Kubernetes operator model reduces overhead, ensuring data safety remains a challenge as data warehouses are complex and losing data is problematic. 3) Future work includes developing data warehouses as a service on Kubernetes with multi-tenancy, high availability, security, and optimized resource utilization, as well as enhancing ClickHouse to better match Kubernetes' cloud-native execution model.

Imply at Apache Druid Meetup in London 1-15-20Jelena Zanko

Druid is a real-time analytics engine that allows for fast querying of large datasets. It is column-oriented, supports high concurrency, and can scale to thousands of servers and millions of messages per second. Druid uses an immutable segment-based architecture to store data efficiently and supports real-time and batch ingestion of data. It allows for complex queries through SQL and JSON and uses approximations and data sketches to improve query performance for large datasets.

Webinar: Strength in Numbers: Introduction to ClickHouse Cluster PerformanceAltinity Ltd

Webinar, April 29, 2020 ClickHouse clusters apply the power of dozens or even hundreds of nodes to vast datasets. In this webinar we'll show you how to use the basic tools of replication and sharding to create high performance ClickHouse clusters. We'll study the plumbing of inserts into sharded datasets and how to determine the correct number of shards for your desired writes. We'll similarly look at distributed queries and show how to scale read capacity to desired levels using replicas. Finally, we'll look at techniques for scaling up both shards and replicas to accommodate growth in your dataset.

How to build analytics for 100bn logs a month with ClickHouse. By Vadim Tkach...Valery Tkachenko

Vectorization improves performance by representing data as arrays that can be processed in tight loops by CPUs. This allows compilers to generate SIMD instructions to optimize processing multiple values simultaneously. Modern CPUs also benefit from vectorization by executing multiple loop iterations concurrently through out-of-order execution. Studies have shown vectorized execution can improve performance of data-intensive queries in ClickHouse by up to a factor of 50 compared to non-vectorized execution.

How to build analytics for 100bn logs a month with ClickHouse. By Vadim Tkach...Altinity Ltd

Ca alternative architectureUniversity of Sargodha

The document discusses RISC and CISC architectures, Flynn's taxonomy of parallel processing, and alternative parallel processing approaches. It notes that RISC machines use explicit load and store instructions to access memory, while CISC machines use variable-length instructions. Flynn's taxonomy categorizes architectures based on the number of instruction and data streams. The document also describes superscalar, VLIW, vector processors, distributed computing, dataflow computing, neural networks, and systolic arrays as parallel processing approaches.

Real-Time Scheduling AlgorithmsAJAL A J

More Related Content

What's hot (19)

ClickHouse Introduction by Alexander Zaitsev, Altinity CTOAltinity Ltd

Webinar 2017. Supercharge your analytics with ClickHouse. Alexander ZaitsevAltinity Ltd

ClickHouse Introduction, by Alexander Zaitsev, Altinity CTOAltinity Ltd

Building an Observability platform with ClickHouseAltinity Ltd

Clickhouse at Cloudflare. By Marek VavrusaValery Tkachenko

Dangerous on ClickHouse in 30 minutes, by Robert Hodges, Altinity CEOAltinity Ltd

ClickHouse Deep Dive, by Aleksei MilovidovAltinity Ltd

Clickhouse Capacity Planning for OLAP Workloads, Mik Kocikowski of CloudFlareAltinity Ltd

High Performance, High Reliability Data Loading on ClickHouseAltinity Ltd

HTTP Analytics for 6M requests per second using ClickHouse, by Alexander Boc...Altinity Ltd

ClickHouse Analytical DBMS: Introduction and Case Studies, by Alexander ZaitsevAltinity Ltd

A Fast Intro to Fast Query with ClickHouse, by Robert HodgesAltinity Ltd

Clickhouse MeetUp@ContentSquare - ContentSquare's Experience SharingVianney FOUCAULT

ClickHouse on Plug-n-Play Cloud, by Som Sikdar, Kodiak DataAltinity Ltd

Bitquery GraphQL for Analytics on ClickHouseAltinity Ltd

Data Warehouse on Kubernetes: lessons from Clickhouse OperatorAltinity Ltd

Imply at Apache Druid Meetup in London 1-15-20Jelena Zanko

Webinar: Strength in Numbers: Introduction to ClickHouse Cluster PerformanceAltinity Ltd

How to build analytics for 100bn logs a month with ClickHouse. By Vadim Tkach...Valery Tkachenko

ClickHouse Introduction by Alexander Zaitsev, Altinity CTOAltinity Ltd

Webinar 2017. Supercharge your analytics with ClickHouse. Alexander ZaitsevAltinity Ltd

ClickHouse Introduction, by Alexander Zaitsev, Altinity CTOAltinity Ltd

Building an Observability platform with ClickHouseAltinity Ltd

Clickhouse at Cloudflare. By Marek VavrusaValery Tkachenko

Dangerous on ClickHouse in 30 minutes, by Robert Hodges, Altinity CEOAltinity Ltd

ClickHouse Deep Dive, by Aleksei MilovidovAltinity Ltd

Clickhouse Capacity Planning for OLAP Workloads, Mik Kocikowski of CloudFlareAltinity Ltd

High Performance, High Reliability Data Loading on ClickHouseAltinity Ltd

HTTP Analytics for 6M requests per second using ClickHouse, by Alexander Boc...Altinity Ltd

ClickHouse Analytical DBMS: Introduction and Case Studies, by Alexander ZaitsevAltinity Ltd

A Fast Intro to Fast Query with ClickHouse, by Robert HodgesAltinity Ltd

Clickhouse MeetUp@ContentSquare - ContentSquare's Experience SharingVianney FOUCAULT

ClickHouse on Plug-n-Play Cloud, by Som Sikdar, Kodiak DataAltinity Ltd

Bitquery GraphQL for Analytics on ClickHouseAltinity Ltd

Data Warehouse on Kubernetes: lessons from Clickhouse OperatorAltinity Ltd

Imply at Apache Druid Meetup in London 1-15-20Jelena Zanko

Webinar: Strength in Numbers: Introduction to ClickHouse Cluster PerformanceAltinity Ltd

How to build analytics for 100bn logs a month with ClickHouse. By Vadim Tkach...Valery Tkachenko

Similar to Supercharge your Analytics with ClickHouse, v.2. By Vadim Tkachenko (20)

How to build analytics for 100bn logs a month with ClickHouse. By Vadim Tkach...Altinity Ltd

Ca alternative architectureUniversity of Sargodha

Real-Time Scheduling AlgorithmsAJAL A J

Sql server engine cpu cache as the new ramChris Adkin

Data Grids with Oracle CoherenceBen Stopford

The document discusses data partitioning and distribution across multiple machines in a cluster. It explains that data replication does not scale well, but data partitioning, where each record exists on only one machine, allows write latency to scale with the number of machines in the cluster. Coherence provides a distributed cache that partitions data and offers functions for server-side processing near the data through tools like entry processors.

Flynn taxonomiesSrujan Damera

The document discusses different types of parallel architectures including SISD, SIMD, MISD, and MIMD. SISD refers to a single instruction single data stream and includes traditional uniprocessors. SIMD uses a single instruction on multiple data streams, like in vector processors. MISD has multiple instructions on a single data stream, like systolic arrays. MIMD uses multiple instructions and data streams, including traditional multiprocessors and networks of workstations. The document explores techniques for exploiting parallelism in different architectures and trends towards superscalar designs and instruction-level parallelism. It argues future systems will require even more parallelism to continue improving performance.

SparkSQL: A Compiler from Queries to RDDsDatabricks

SparkSQL, a module for processing structured data in Spark, is one of the fastest SQL on Hadoop systems in the world. This talk will dive into the technical details of SparkSQL spanning the entire lifecycle of a query execution. The audience will walk away with a deeper understanding of how Spark analyzes, optimizes, plans and executes a user’s query. Speaker: Sameer Agarwal This talk was originally presented at Spark Summit East 2017.

Performance Optimization of Deep Learning Frameworks Caffe* and Tensorflow* f...Intel® Software

RedisConf17 - Doing More With Redis - Ofer Bengal and Yiftach ShoolmanRedis Labs

This document summarizes RedisConf 2017, covering several topics: 1. Running Redis on Flash in a DBaaS model for improved performance and cost savings compared to other NoSQL databases. 2. Redis modules gaining momentum with over 50 created so far, and the importance of multi-threading for high performance. Useful modules highlighted include RediSearch, ReJSON, Redis-ML, and Redis-Graph. 3. Using Redis for IoT applications, with challenges around small edge devices and clusters, high throughput from thousands of devices, and varied functionality needs addressed through Redis modules.

Aran Khanna, Software Engineer, Amazon Web Services at MLconf ATL 2017MLconf

High Performance Deep Learning on Edge Devices With Apache MXNet: Deep network based models are marked by an asymmetry between the large amount of compute power needed to train a model, and the relatively small amount of compute power needed to deploy a trained model for inference. This is particularly true in computer vision tasks such as object detection or image classification, where millions of labeled images and large numbers of GPUs are needed to produce an accurate model that can be deployed for inference on low powered devices with a single CPU. The challenge when deploying vision models on these low powered devices though, is getting inference to run efficiently enough to allow for near real time processing of a video stream. Fortunately Apache MXNet provides the tools to solve this issues, allowing users to create highly performant models with tools like separable convolutions, quantized weights and sparsity exploitation as well as providing custom hardware kernels to ensure inference calculations are accelerated to the maximum amount allowed by the hardware the model is being deployed on. This is demonstrated though a state of the art MXNet based vision network running in near real time on a low powered Raspberry Pi device. We finally discuss how running inference at the edge as well as leveraging MXNet’s efficient modeling tools can be used to massively drive down compute costs for deploying deep networks in a production system at scale.

Programmable Exascale SupercomputerSagar Dolas

We leave in the era where the atomic building elements of silicon computers, e.g., transistors and wires, are no longer visible using traditional optical microscopes and their sizes are measured in just tens of Angstroms. In addition, power dissipation per unit volume is bounded by the laws of Physics that all resulted among others in stagnating processor clock frequencies. Adding more and more processor cores that perform simpler and simpler tasks in an attempt to efficiently fill the available on-chip area seems to be the current trend taken by the Industry.

Autonomous control in Big Data platforms: and experience with CassandraEmiliano

Accelerating EDA workloads on Azure – Best Practice and benchmark on Intel EM...Meng-Ru (Raymond) Tsai

Benchmark EDA workloads on Azure Intel Emerald Rapids (EMR) VMs. The article evaluates the performance of the latest Azure VMs using the 5th Gen Intel® Xeon® Platinum 8537C (Emerald Rapids) processor by comparing them to the previous Ice Lake generation. Using two EDA tools, Cadence Spectre-X and Synopsys VCS, the benchmarks involve real-world scenarios including single-threaded, multi-threaded, and multiple jobs running on one node. Results show that Spectre-X performs 12 to 18% better on D64ds v6 instances and 22 to 29% better on FX64v2 instances compared to D64ds v5 instances. The D64ds v6 instances were found to be more cost-effective, while FX64mds v2 instances achieved the shortest total runtime. For Synopsys VCS, the benchmarks revealed a speedup of 17 to 43% for Emerald Rapids instances over Ice Lake instances across various parallel simulations. The findings offer EDA customers options on which Azure EMR instances to select based on the cost-efficiency analysis.

Par comtttoracle

Parallel processing architectures allow for simultaneous computation across multiple processing elements. There are four main types of parallel architectures: single instruction single data (SISD), single instruction multiple data (SIMD), multiple instruction single data (MISD), and multiple instruction multiple data (MIMD). MIMD systems are the most common and can have either shared or distributed memory. Effective parallel programming requires approaches like message passing or shared memory models to facilitate communication between processing elements.

Webinar on RISC-VDeepak Shankar

Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...Altinity Ltd

ClickHouse is a powerful open source analytics database that provides fast, scalable performance for data warehousing and real-time analytics use cases. It can handle petabytes of data and queries and scales linearly on commodity hardware. ClickHouse is faster than other databases for analytical workloads due to its columnar data storage and parallel processing. It supports SQL and integrates with various data sources. ClickHouse can run on-premises, in the cloud, or in containers. The ClickHouse operator makes it easy to deploy and manage ClickHouse clusters on Kubernetes.

Machine Learning & Data Science in the Age of the GPU: Smarter, Faster, BetterIDEAS - Int'l Data Engineering and Science Association

This document discusses how MapD uses GPUs to accelerate machine learning and data science. It summarizes that: 1) MapD Core is a columnar database powered by GPUs that can run queries 100x faster than other databases. 2) MapD Immerse is a GPU-powered visualization front-end that enables visualization at high speeds. 3) Benchmarks showed MapD was orders of magnitude faster than other databases, frameworks, and warehouses at querying a billion row taxi dataset.

Energy efficient AI workload partitioning on multi-core systemsDeepak Shankar

o create an AI system, the semiconductor, software, and systems team need to work together. Multi-core systems can provide extremely low latency and higher throughput at lower power consumption. But concurrent access to shared resources by multiple of AI workloads running on different cores can create higher worst-case execution time (WCET) and causes multiple system failures. Architecture exploration can be used to efficiently balance the compute, communication, synchronization, and storage. In this Webinar, we will be using Workloads from automotive, and data centers to demonstrate the methodology. VisualSim Architect enables designers to assemble architecture models that extend from the smallest IoT to full automotive, and Radar systems to Data Centers. These models will include any combination of software, processors, ECU, RTOS and networks. Using this platform, software designer can explore the partitioning of the AI tasks (software or model) on to cores based on the latency, bandwidth, and power constraints. Within the IoT, the processor, A/D, Bluetooth and software can be modeled while an automotive design will require the network, ECU and firmware. Both have a unique mechanism to define the traffic, test scenarios and AI workloads. Hardware engineers can select cores, cores per cluster, cache hierarchy, memory controller, accelerators, and the interface topology. Software engineers can tune the partitioning, synchronization overhead, memory access schedules and scheduling.

Exploring Emerging Technologies in the Extreme Scale HPC Co-Design Space with...jsvetter

Oracle Query Optimizer - An Introductionadryanbub

How to build analytics for 100bn logs a month with ClickHouse. By Vadim Tkach...Altinity Ltd

Ca alternative architectureUniversity of Sargodha

Real-Time Scheduling AlgorithmsAJAL A J

Sql server engine cpu cache as the new ramChris Adkin

Data Grids with Oracle CoherenceBen Stopford

Flynn taxonomiesSrujan Damera

SparkSQL: A Compiler from Queries to RDDsDatabricks

Performance Optimization of Deep Learning Frameworks Caffe* and Tensorflow* f...Intel® Software

RedisConf17 - Doing More With Redis - Ofer Bengal and Yiftach ShoolmanRedis Labs

Aran Khanna, Software Engineer, Amazon Web Services at MLconf ATL 2017MLconf

Programmable Exascale SupercomputerSagar Dolas

Autonomous control in Big Data platforms: and experience with CassandraEmiliano

Accelerating EDA workloads on Azure – Best Practice and benchmark on Intel EM...Meng-Ru (Raymond) Tsai

Par comtttoracle

Webinar on RISC-VDeepak Shankar

Analytics at Speed: Introduction to ClickHouse and Common Use Cases. By Mikha...Altinity Ltd

Machine Learning & Data Science in the Age of the GPU: Smarter, Faster, BetterIDEAS - Int'l Data Engineering and Science Association

Energy efficient AI workload partitioning on multi-core systemsDeepak Shankar

Exploring Emerging Technologies in the Extreme Scale HPC Co-Design Space with...jsvetter

Oracle Query Optimizer - An Introductionadryanbub

More from Altinity Ltd (20)

Building an Analytic Extension to MySQL with ClickHouse and Open Source.pptxAltinity Ltd

Building an Analytic Extension to MySQL with ClickHouse and Open Source In this webinar Percona and Altinity offer suggestions and tips on how to recognize when MySQL is overburdened with analytics and can benefit from ClickHouse’s unique capabilities. Also, they will walk you through important patterns for integrating MySQL and ClickHouse which will enable the building of powerful and cost-efficient applications that leverage the strengths of both databases.

Cloud Native ClickHouse at Scale--Using the Altinity Kubernetes Operator-2022...Altinity Ltd

Over the last few years Kubernetes has transitioned from an object of curiosity and fear to a robust platform for big data. Watch this webinar and you will learn how the Altinity Kubernetes Operator for ClickHouse enables users to run high performance analytics on ClickHouse. You will see a simple installation and teach you how to scale it into a cluster that can analyze 100s of terabytes of data. Along the way we’ll share our lessons for ClickHouse on Kubernetes in Altinity.Cloud. We built it on Kubernetes using the Altinity Operator and now run hundreds of clusters in the cloud. You can too!

Building an Analytic Extension to MySQL with ClickHouse and Open SourceAltinity Ltd

This is a joint webinar Percona - Altinity. In this webinar we will discuss suggestions and tips on how to recognize when MySQL is overburdened with analytics and can benefit from ClickHouse’s unique capabilities. We will then walk through important patterns for integrating MySQL and ClickHouse which will enable the building of powerful and cost-efficient applications that leverage the strengths of both databases.

Fun with ClickHouse Window Functions-2021-08-19.pdfAltinity Ltd

Fun with ClickHouse Window Functions | Altinity Webinar Window functions have arrived in ClickHouse! Our webinar will start with an introduction to standard window function syntax and show how it is implemented in ClickHouse. We’ll next show you problems that you can now solve easily using window functions. Finally, we’ll compare window functions to arrays, another powerful ClickHouse feature. There will be time for questions with our SQL experts. Join us for a complete overview of this long-awaited feature! Speakers: Robert Hodges, CEO @Altinity Vitaliy Zakaznikov, QA Manager and Architect @Altinity

Cloud Native Data Warehouses - Intro to ClickHouse on Kubernetes-2021-07.pdfAltinity Ltd

Robert Hodges from Altinity, an enterprise provider of ClickHouse and developer of the ClickHouse Kubernetes operator, provides an introduction to running ClickHouse on Kubernetes. The presentation demonstrates how to deploy ClickHouse and Zookeeper on Kubernetes using the ClickHouse Kubernetes operator. It shows how to define ClickHouse installations using custom resources, access ClickHouse, and update the cluster configuration, such as changing the number of shards and replicas or ClickHouse version. The operator automatically applies configuration changes to pods.

Building High Performance Apps with Altinity Stable Builds for ClickHouse | A...Altinity Ltd

Altinity Stable Builds offer a ClickHouse distribution that is ready for production use and with 3 years of maintenance. Our webinar introduces the special features of Stable Builds and describes how we build them from ClickHouse Long-Term Support (LTS) releases. We’ll show you how to find them and install them yourself, then guide you through the important topic of upgrading. We’ll also walk through how to use Altinity Stable Builds in Altinity.Cloud, our managed ClickHouse platform for high-performance analytics.

Application Monitoring using Open Source - VictoriaMetrics & Altinity ClickHo...Altinity Ltd

Application Monitoring using Open Source - VictoriaMetrics & Altinity ClickHouse Webinar Slides Monitoring is the key to the successful operation of any software service, but commercial solutions are complex, expensive, and slow. Let us show you how to build monitoring that is simple, cost-effective, and fast using open-source stacks easily accessible to any developer. We’ll start with the elements of monitoring systems: data ingest, query engine, visualization, and alerting. We’ll then explain and contrast two implementation approaches. The first uses VictoriaMetrics, a fast-growing, high-performance time series database that uses PromQL for queries. The second is based on ClickHouse, a popular real-time analytics database that speaks SQL. Fast, affordable monitoring is within reach. This webinar provides designs and working code to get you there. Presented by: Roman Khavronenko, Co-Founder at VictoriaMetrics Robert Hodges, CEO at Altinity

Own your ClickHouse data with Altinity.Cloud Anywhere-2023-01-17.pdfAltinity Ltd

The document discusses Altinity.Cloud Anywhere, a service that allows users to run ClickHouse databases on their own Kubernetes clusters. It provides automation of ClickHouse operations and management through the Altinity Connector. Users can prepare their own Kubernetes environment, connect it to Altinity.Cloud, and then launch and manage ClickHouse clusters on their infrastructure. Advanced topics covered include how the service works internally and how to get support from Altinity.

ClickHouse ReplacingMergeTree in Telecom AppsAltinity Ltd

Adventures with the ClickHouse ReplacingMergeTree EngineAltinity Ltd

Building a Real-Time Analytics Application with Apache Pulsar and Apache PinotAltinity Ltd

This document provides an overview of building a real-time analytics application with Apache Pulsar and Apache Pinot. It introduces Mary Grygleski and Mark Needham, describes what real-time analytics is, and discusses the properties of real-time analytics systems. It then demonstrates how to ingest data from the Wikimedia recent changes feed into Pulsar and Pinot for real-time analytics and builds a dashboard with the data using Streamlit.

Altinity Webinar: Introduction to Altinity.Cloud-Platform for Real-Time Data.pdfAltinity Ltd

Altinity Webinar: Introduction to Altinity.Cloud-Platform for Real-Time Data - Presentation Slides Altinity.Cloud is a fully automated cloud service for ClickHouse that is optimized for real-time analytics. In this webinar, we’ll explain how Altinity.Cloud works, then show how to set up your first ClickHouse cluster. We’ll then tour important features like scale-up, scale-out, uptime schedules, and DBA tools to analyze your tables. You’ll learn everything necessary to start working on real-time analytics today. Bring your questions! Presenters: Robert Hodges & Alexander Zaitsev Note: This webinar will be recorded and later posted on our Webinar page (https://altinity.com/webinarspage/) or Altinity official Youtube channel (https://www.youtube.com/@Altinity).

OSA Con 2022 - What Data Engineering Can Learn from Frontend Engineering - Pe...Altinity Ltd

OSA Con 2022 - Welcome to OSA CON Version 2022 - Robert Hodges - Altinity.pdfAltinity Ltd

OSA Con 2022 - Using ClickHouse Database to Power Analytics and Customer Enga...Altinity Ltd

OSA Con 2022: Using ClickHouse Database to Power Analytics and Customer Engagement Platform Prafulla Gupta - Times Internet This talk covers how we empowered Product Managers and Editors at Times Internet by developing an in-house product, GrowthRx, using Clickhouse Open Source Database to track and analyze user behavior to increase user retention and customer engagement. Times Internet is India's largest digital news publisher, which manages leading brands like Times of India, Economic Times, Navbharat Times, etc, where we are tracking more than 10 billion events per month in the ClickHouse Database.

OSA Con 2022 - Tips and Tricks to Keep Your Queries under 100ms with ClickHou...Altinity Ltd

- ClickHouse can query 170 billion rows at 500 queries per second with a 99th percentile latency of 110ms through careful data modeling, query optimization, and use of materialized views. - To achieve low latency at high query rates, it is important to reduce the amount of data scanned by queries through techniques like sorting keys, data compression, and reducing data cardinality. - Materialized views can reduce data sizes by 1000-10,000x and are critical for maintaining low query latencies on large datasets. Dividing data into read and write replicas also improves query performance.

OSA Con 2022 - The Open Source Analytic Universe, Version 2022 - Robert Hodge...Altinity Ltd

OSA Con 2022 - Switching Jaeger Distributed Tracing to ClickHouse to Enable A...Altinity Ltd

The document discusses how OpsVerse migrated their Jaeger distributed tracing storage from Cassandra to ClickHouse for improved performance monitoring. Jaeger is an open source distributed tracing system that was originally designed to use Elasticsearch or Cassandra for storage. While Cassandra worked well for basic functionality, it lacked capabilities for advanced analytics. ClickHouse supports richer query functions and better handles large datasets. The document outlines the steps OpsVerse took to implement the ClickHouse storage plugin for Jaeger and deploy ClickHouse on Kubernetes using the ClickHouse Operator. This migration enabled more insightful performance monitoring and analytics.

OSA Con 2022 - Streaming Data Made Easy - Tim Spann & David Kjerrumgaard - St...Altinity Ltd

OSA Con 2022 - State of Open Source Databases - Peter Zaitsev - Percona.pdfAltinity Ltd

OSA Con 2022 - State of Open Source Databases Peter Zaitsev - Percona It has been an exciting year in the open-source database industry, with more choices, more cloud, and key changes in the industry. We will dive into the key developments over 2022, including the most important open-source database software releases in general, the significance of cloud-native solutions in a multi-vendor multi-cloud world, the new criticality of security challenges, and the evolution of the open-source software industry.