Fast Streaming into Clickhouse with Apache PulsarTimothy Spann
https://github.com/tspannhw/SpeakerProfile/tree/main/2022/talks
Fast Streaming into Clickhouse with Apache Pulsar
https://github.com/tspannhw/FLiPC-FastStreamingIntoClickhouseWithApachePulsar
https://www.meetup.com/San-Francisco-Bay-Area-ClickHouse-Meetup/events/285271332/
Fast Streaming into Clickhouse with Apache Pulsar - Meetup 2022
StreamNative - Apache Pulsar - Stream to Altinity Cloud - Clickhouse
May the 4th Be With You!
04-May-2022 Clickhosue Meetup
CREATE TABLE iotjetsonjson_local
(
uuid String,
camera String,
ipaddress String,
networktime String,
top1pct String,
top1 String,
cputemp String,
gputemp String,
gputempf String,
cputempf String,
runtime String,
host String,
filename String,
host_name String,
macaddress String,
te String,
systemtime String,
cpu String,
diskusage String,
memory String,
imageinput String
)
ENGINE = MergeTree()
PARTITION BY uuid
ORDER BY (uuid);
CREATE TABLE iotjetsonjson ON CLUSTER '{cluster}' AS iotjetsonjson_local
ENGINE = Distributed('{cluster}', default, iotjetsonjson_local, rand());
select uuid, top1pct, top1, gputempf, cputempf
from iotjetsonjson
where toFloat32OrZero(top1pct) > 40
order by toFloat32OrZero(top1pct) desc, systemtime desc
select uuid, systemtime, networktime, te, top1pct, top1, cputempf, gputempf, cpu, diskusage, memory,filename
from iotjetsonjson
order by systemtime desc
select top1, max(toFloat32OrZero(top1pct)), max(gputempf), max(cputempf)
from iotjetsonjson
group by top1
select top1, max(toFloat32OrZero(top1pct)) as maxTop1, max(gputempf), max(cputempf)
from iotjetsonjson
group by top1
order by maxTop1
Tim Spann
Developer Advocate
StreamNative
This presentation describes how to efficiently load data into Hive. I cover partitioning, predicate pushdown, ORC file optimization and different loading schemes
This talk provides an in-depth overview of the key concepts of Apache Calcite. It explores the Calcite catalog, parsing, validation, and optimization with various planners.
Simplify CDC Pipeline with Spark Streaming SQL and Delta LakeDatabricks
Change Data Capture CDC is a typical use case in Real-Time Data Warehousing. It tracks the data change log -binlog- of a relational database [OLTP], and replay these change log timely to an external storage to do Real-Time OLAP, such as delta/kudu. To implement a robust CDC streaming pipeline, lots of factors should be concerned, such as how to ensure data accuracy , how to process OLTP source schema changed, whether it is easy to build for variety databases with less code.
Fast Streaming into Clickhouse with Apache PulsarTimothy Spann
https://github.com/tspannhw/SpeakerProfile/tree/main/2022/talks
Fast Streaming into Clickhouse with Apache Pulsar
https://github.com/tspannhw/FLiPC-FastStreamingIntoClickhouseWithApachePulsar
https://www.meetup.com/San-Francisco-Bay-Area-ClickHouse-Meetup/events/285271332/
Fast Streaming into Clickhouse with Apache Pulsar - Meetup 2022
StreamNative - Apache Pulsar - Stream to Altinity Cloud - Clickhouse
May the 4th Be With You!
04-May-2022 Clickhosue Meetup
CREATE TABLE iotjetsonjson_local
(
uuid String,
camera String,
ipaddress String,
networktime String,
top1pct String,
top1 String,
cputemp String,
gputemp String,
gputempf String,
cputempf String,
runtime String,
host String,
filename String,
host_name String,
macaddress String,
te String,
systemtime String,
cpu String,
diskusage String,
memory String,
imageinput String
)
ENGINE = MergeTree()
PARTITION BY uuid
ORDER BY (uuid);
CREATE TABLE iotjetsonjson ON CLUSTER '{cluster}' AS iotjetsonjson_local
ENGINE = Distributed('{cluster}', default, iotjetsonjson_local, rand());
select uuid, top1pct, top1, gputempf, cputempf
from iotjetsonjson
where toFloat32OrZero(top1pct) > 40
order by toFloat32OrZero(top1pct) desc, systemtime desc
select uuid, systemtime, networktime, te, top1pct, top1, cputempf, gputempf, cpu, diskusage, memory,filename
from iotjetsonjson
order by systemtime desc
select top1, max(toFloat32OrZero(top1pct)), max(gputempf), max(cputempf)
from iotjetsonjson
group by top1
select top1, max(toFloat32OrZero(top1pct)) as maxTop1, max(gputempf), max(cputempf)
from iotjetsonjson
group by top1
order by maxTop1
Tim Spann
Developer Advocate
StreamNative
This presentation describes how to efficiently load data into Hive. I cover partitioning, predicate pushdown, ORC file optimization and different loading schemes
This talk provides an in-depth overview of the key concepts of Apache Calcite. It explores the Calcite catalog, parsing, validation, and optimization with various planners.
Simplify CDC Pipeline with Spark Streaming SQL and Delta LakeDatabricks
Change Data Capture CDC is a typical use case in Real-Time Data Warehousing. It tracks the data change log -binlog- of a relational database [OLTP], and replay these change log timely to an external storage to do Real-Time OLAP, such as delta/kudu. To implement a robust CDC streaming pipeline, lots of factors should be concerned, such as how to ensure data accuracy , how to process OLTP source schema changed, whether it is easy to build for variety databases with less code.
Tuning Apache Kafka Connectors for Flink.pptxFlink Forward
Flink Forward San Francisco 2022.
In normal situations, the default Kafka consumer and producer configuration options work well. But we all know life is not all roses and rainbows and in this session we’ll explore a few knobs that can save the day in atypical scenarios. First, we'll take a detailed look at the parameters available when reading from Kafka. We’ll inspect the params helping us to spot quickly an application lock or crash, the ones that can significantly improve the performance and the ones to touch with gloves since they could cause more harm than benefit. Moreover we’ll explore the partitioning options and discuss when diverging from the default strategy is needed. Next, we’ll discuss the Kafka Sink. After browsing the available options we'll then dive deep into understanding how to approach use cases like sinking enormous records, managing spikes, and handling small but frequent updates.. If you want to understand how to make your application survive when the sky is dark, this session is for you!
by
Olena Babenko
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
Massive Data Processing in Adobe Using Delta LakeDatabricks
At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences.
What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
Go over anti-patterns used
(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
Storage Capacity Management on Multi-tenant Kafka Cluster with Nurettin OmerogluHostedbyConfluent
"I will be presenting how we do the smart/automated capacity management on Multi-tenant Kafka cluster in Booking.com. It was a long journey. In this end to end story, I will be presenting what the issues were at the beginning, how we came up with a plan, designed, implemented, and applied to our existing clusters smoothly, now how the clients can monitor and even get alerted before their reserved capacity has been reached. What were the challenges and our learnings? What is next?
Why? In Booking.com, the infra team manages 60 different Kafka clusters with hundreds of topics in each. There are clusters running with hundred brokers. As there are hundreds of Kafka clients from tens of different departments, it is high likely some of the clients start abusing the cluster. Especially during peak times, when the retention was set as retention.ms, or when the underlying message size changes, it is hard to predict what would be the occupied storage in total. Finding the relevant clients, deciding which data to discard, dealing with so many unknowns in a short period of time can be hassle. Also these are not fun activities but just a toil for the team.
What? To avoid such boring issues, the team has chosen the path to build a smart mechanism and have quotas in place. It helped saving time developing new features instead of chasing people to resolve collisions. You can think that as an extension to the built-in throttling producer/consumer rate limits provided by the Apache Kafka, but it is much more than that. There are several components will be explained during the presentation one of them is our control plane (custom built) which manages the communication between clients and servers and does many things automated.
Another one is the Custom Policies that we plugged in on the Kafka side to validate the configuration even tried (malicious configuration) on the server side. The talk guarantees learning and shows examples of Kafka at scale problems in Booking.com."
Meta/Facebook's database serving social workloads is running on top of MyRocks (MySQL on RocksDB). This means our performance and reliability depends a lot on RocksDB. Not just MyRocks, but also we have other important systems running on top of RocksDB. We have learned many lessons from operating and debugging RocksDB at scale.
In this session, we will offer an overview of RocksDB, key differences from InnoDB, and share a few interesting lessons learned from production.
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
Common issues with Apache Kafka® Producerconfluent
Badai Aqrandista, Confluent, Senior Technical Support Engineer
This session will be about a common issue in the Kafka Producer: producer batch expiry. We will be discussing the Kafka Producer internals, its common causes, such as a slow network or small batching, and how to overcome them. We will also be sharing some examples along the way!
https://www.meetup.com/apache-kafka-sydney/events/279651982/
Automate Your Kafka Cluster with Kubernetes Custom Resources confluent
(Sam Obeid, Shopify) Kafka Summit SF 2018
At Shopify we manage multiple Apache Kafka clusters in multiple locations in Google’s cloud platform. We deploy our Kafka clusters as Kubernetes StatefulSets, and we use other K8s workloads to implement different tasks. Automating critical and repetitive operational tasks is one of our top priorities.
In this talk we’ll discuss how we leveraged Kubernetes Custom Resources and Controllers to automate some of the key cluster operational tasks, to detect clusters configuration changes and react to these changes with required actions. We will go through actual examples we implemented at Shopify, how we solved the problem of cluster discovery and how we automated topics creation across different clusters with zero human intervention and safety controls.
Tame the small files problem and optimize data layout for streaming ingestion...Flink Forward
Flink Forward San Francisco 2022.
In modern data platform architectures, stream processing engines such as Apache Flink are used to ingest continuous streams of data into data lakes such as Apache Iceberg. Streaming ingestion to iceberg tables can suffer by two problems (1) small files problem that can hurt read performance (2) poor data clustering that can make file pruning less effective. To address those two problems, we propose adding a shuffling stage to the Flink Iceberg streaming writer. The shuffling stage can intelligently group data via bin packing or range partition. This can reduce the number of concurrent files that every task writes. It can also improve data clustering. In this talk, we will explain the motivations in details and dive into the design of the shuffling stage. We will also share the evaluation results that demonstrate the effectiveness of smart shuffling.
by
Gang Ye & Steven Wu
Distributed Databases Deconstructed: CockroachDB, TiDB and YugaByte DBYugabyteDB
Slides for Amey Banarse's, Principal Data Architect at Yugabyte, "Distributed Databases Deconstructed: CockroachDB, TiDB and YugaByte DB" webinar recorded on Oct 30, 2019 at 11 AM Pacific.
Playback here: https://vimeo.com/369929255
Building Robust ETL Pipelines with Apache SparkDatabricks
Stable and robust ETL pipelines are a critical component of the data infrastructure of modern enterprises. ETL pipelines ingest data from a variety of sources and must handle incorrect, incomplete or inconsistent records and produce curated, consistent data for consumption by downstream applications. In this talk, we’ll take a deep dive into the technical details of how Apache Spark “reads” data and discuss how Spark 2.2’s flexible APIs; support for a wide variety of datasources; state of art Tungsten execution engine; and the ability to provide diagnostic feedback to users, making it a robust framework for building end-to-end ETL pipelines.
This talk will tell the story of an analytics use case database from a non-OLAP and ACID-compliant RDBMS (MySQL) perspective.
I will cover the basics of the Clickhouse database Sample Clickhouse installation in a lab environment.
We are configuring Clickhouse for essential operations.
We will load the sample data set and monitor it.
We will query and visualize the results.
This talk will also base on how Kubernetes can help Clickhouse implementation via an operator.
Conclusions will include Do's and Don't of this emerging technology. Best practices and some advice around ingesting and analyzing terabytes of data efficiently.
Flink Forward San Francisco 2022.
Resource Elasticity is a frequently requested feature in Apache Flink: Users want to be able to easily adjust their clusters to changing workloads for resource efficiency and cost saving reasons. In Flink 1.13, the initial implementation of Reactive Mode was introduced, later releases added more improvements to make the feature production ready. In this talk, we’ll explain scenarios to deploy Reactive Mode to various environments to achieve autoscaling and resource elasticity. We’ll discuss the constraints to consider when planning to use this feature, and also potential improvements from the Flink roadmap. For those interested in the internals of Flink, we’ll also briefly explain how the feature is implemented, and if time permits, conclude with a short demo.
by
Robert Metzger
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...Databricks
Spark SQL is a highly scalable and efficient relational processing engine with ease-to-use APIs and mid-query fault tolerance. It is a core module of Apache Spark. Spark SQL can process, integrate and analyze the data from diverse data sources (e.g., Hive, Cassandra, Kafka and Oracle) and file formats (e.g., Parquet, ORC, CSV, and JSON). This talk will dive into the technical details of SparkSQL spanning the entire lifecycle of a query execution. The audience will get a deeper understanding of Spark SQL and understand how to tune Spark SQL performance.
Kafka Connect: Real-time Data Integration at Scale with Apache Kafka, Ewen Ch...confluent
Many companies are adopting Apache Kafka to power their data pipelines, including LinkedIn, Netflix, and Airbnb. Kafka’s ability to handle high throughput real-time data makes it a perfect fit for solving the data integration problem, acting as the common buffer for all your data and bridging the gap between streaming and batch systems.
However, building a data pipeline around Kafka today can be challenging because it requires combining a wide variety of tools to collect data from disparate data systems. One tool streams updates from your database to Kafka, another imports logs, and yet another exports to HDFS. As a result, building a data pipeline can take significant engineering effort and has high operational overhead because all these different tools require ongoing monitoring and maintenance. Additionally, some of the tools are simply a poor fit for the job: the fragmented nature of the data integration tools ecosystem lead to creative but misguided solutions such as misusing stream processing frameworks for data integration purposes.
We describe the design and implementation of Kafka Connect, Kafka’s new tool for scalable, fault-tolerant data import and export. First we’ll discuss some existing tools in the space and why they fall short when applied to data integration at large scale. Next, we will explore Kafka Connect’s design and how it compares to systems with similar goals, discussing key design decisions that trade off between ease of use for connector developers, operational complexity, and reuse of existing connectors. Finally, we’ll discuss how standardizing on Kafka Connect can ultimately lead to simplifying your entire data pipeline, making ETL into your data warehouse and enabling stream processing applications as simple as adding another Kafka connector.
eventbrite_kafka_summit_event_logo_v3-035858-edited.png
The Parquet Format and Performance Optimization OpportunitiesDatabricks
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
Dennis Wittekind, Confluent, Senior Customer Success Engineer
Perhaps you have heard of Kafka Connect and think it would be a great fit in your application's architecture, but you like to know how things work before you propose them to your team? Perhaps you know enough Connect to be dangerous, but you haven't had the time to really understand all the moving pieces? This meetup talk is for you! We'll briefly introduce Connect to the uninitiated, and then jump in to underlying concepts and considerations you should make when running Connect in production! We'll even run a live demo! What could go wrong!?
https://www.meetup.com/Saint-Louis-Kafka-meetup-group/events/272687113/
How to build a streaming Lakehouse with Flink, Kafka, and HudiFlink Forward
Flink Forward San Francisco 2022.
With a real-time processing engine like Flink and a transactional storage layer like Hudi, it has never been easier to build end-to-end low-latency data platforms connecting sources like Kafka to data lake storage. Come learn how to blend Lakehouse architectural patterns with real-time processing pipelines with Flink and Hudi. We will dive deep on how Flink can leverage the newest features of Hudi like multi-modal indexing that dramatically improves query and write performance, data skipping that reduces the query latency by 10x for large datasets, and many more innovations unique to Flink and Hudi.
by
Ethan Guo & Kyle Weller
Scalable and High available Distributed File System Metadata Service Using gR...Alluxio, Inc.
Alluxio Community Office Hour
Apr 7, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speaker: Bin Fan
Alluxio (alluxio.io) is an open-source data orchestration system that provides a single namespace federating multiple external distributed storage systems. It is critical for Alluxio to be able to store and serve the metadata of all files and directories from all mounted external storage both at scale and at speed.
This talk shares our design, implementation, and optimization of Alluxio metadata service (master node) to address the scalability challenges. Particularly, we will focus on how to apply and combine techniques including tiered metadata storage (based on off-heap KV store RocksDB), fine-grained file system inode tree locking scheme, embedded state-replicate machine (based on RAFT), exploration and performance tuning in the correct RPC frameworks (thrift vs gRPC) and etc. As a result of the combined above techniques, Alluxio 2.0 is able to store at least 1 billion files with a significantly reduced memory requirement, serving 3000 workers and 30000 clients concurrently.
In this Office Hour, we will go over how to:
- Metadata storage challenges
- How to combine different open source technologies as building blocks
- The design, implementation, and optimization of Alluxio metadata service
Alluxio - Scalable Filesystem Metadata ServicesAlluxio, Inc.
This talk was presented by Alluxio's top contributor and PMC Maintainer Calvin Jia at the Alluxio bay area Meetup.
This talk shares our design, implementation and optimization of Alluxio metadata service to address the scalability challenges, focusing on how to apply and combine techniques including tiered metadata storage (based on off-heap KV store RocksDB), fine-grained file system inode tree locking scheme, embedded state-replicate machine (based on RAFT), exploration and performance tuning in the correct RPC frameworks (thrift vs gRPC) and etc.
Tuning Apache Kafka Connectors for Flink.pptxFlink Forward
Flink Forward San Francisco 2022.
In normal situations, the default Kafka consumer and producer configuration options work well. But we all know life is not all roses and rainbows and in this session we’ll explore a few knobs that can save the day in atypical scenarios. First, we'll take a detailed look at the parameters available when reading from Kafka. We’ll inspect the params helping us to spot quickly an application lock or crash, the ones that can significantly improve the performance and the ones to touch with gloves since they could cause more harm than benefit. Moreover we’ll explore the partitioning options and discuss when diverging from the default strategy is needed. Next, we’ll discuss the Kafka Sink. After browsing the available options we'll then dive deep into understanding how to approach use cases like sinking enormous records, managing spikes, and handling small but frequent updates.. If you want to understand how to make your application survive when the sky is dark, this session is for you!
by
Olena Babenko
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
Massive Data Processing in Adobe Using Delta LakeDatabricks
At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences.
What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
Go over anti-patterns used
(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
Storage Capacity Management on Multi-tenant Kafka Cluster with Nurettin OmerogluHostedbyConfluent
"I will be presenting how we do the smart/automated capacity management on Multi-tenant Kafka cluster in Booking.com. It was a long journey. In this end to end story, I will be presenting what the issues were at the beginning, how we came up with a plan, designed, implemented, and applied to our existing clusters smoothly, now how the clients can monitor and even get alerted before their reserved capacity has been reached. What were the challenges and our learnings? What is next?
Why? In Booking.com, the infra team manages 60 different Kafka clusters with hundreds of topics in each. There are clusters running with hundred brokers. As there are hundreds of Kafka clients from tens of different departments, it is high likely some of the clients start abusing the cluster. Especially during peak times, when the retention was set as retention.ms, or when the underlying message size changes, it is hard to predict what would be the occupied storage in total. Finding the relevant clients, deciding which data to discard, dealing with so many unknowns in a short period of time can be hassle. Also these are not fun activities but just a toil for the team.
What? To avoid such boring issues, the team has chosen the path to build a smart mechanism and have quotas in place. It helped saving time developing new features instead of chasing people to resolve collisions. You can think that as an extension to the built-in throttling producer/consumer rate limits provided by the Apache Kafka, but it is much more than that. There are several components will be explained during the presentation one of them is our control plane (custom built) which manages the communication between clients and servers and does many things automated.
Another one is the Custom Policies that we plugged in on the Kafka side to validate the configuration even tried (malicious configuration) on the server side. The talk guarantees learning and shows examples of Kafka at scale problems in Booking.com."
Meta/Facebook's database serving social workloads is running on top of MyRocks (MySQL on RocksDB). This means our performance and reliability depends a lot on RocksDB. Not just MyRocks, but also we have other important systems running on top of RocksDB. We have learned many lessons from operating and debugging RocksDB at scale.
In this session, we will offer an overview of RocksDB, key differences from InnoDB, and share a few interesting lessons learned from production.
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
Common issues with Apache Kafka® Producerconfluent
Badai Aqrandista, Confluent, Senior Technical Support Engineer
This session will be about a common issue in the Kafka Producer: producer batch expiry. We will be discussing the Kafka Producer internals, its common causes, such as a slow network or small batching, and how to overcome them. We will also be sharing some examples along the way!
https://www.meetup.com/apache-kafka-sydney/events/279651982/
Automate Your Kafka Cluster with Kubernetes Custom Resources confluent
(Sam Obeid, Shopify) Kafka Summit SF 2018
At Shopify we manage multiple Apache Kafka clusters in multiple locations in Google’s cloud platform. We deploy our Kafka clusters as Kubernetes StatefulSets, and we use other K8s workloads to implement different tasks. Automating critical and repetitive operational tasks is one of our top priorities.
In this talk we’ll discuss how we leveraged Kubernetes Custom Resources and Controllers to automate some of the key cluster operational tasks, to detect clusters configuration changes and react to these changes with required actions. We will go through actual examples we implemented at Shopify, how we solved the problem of cluster discovery and how we automated topics creation across different clusters with zero human intervention and safety controls.
Tame the small files problem and optimize data layout for streaming ingestion...Flink Forward
Flink Forward San Francisco 2022.
In modern data platform architectures, stream processing engines such as Apache Flink are used to ingest continuous streams of data into data lakes such as Apache Iceberg. Streaming ingestion to iceberg tables can suffer by two problems (1) small files problem that can hurt read performance (2) poor data clustering that can make file pruning less effective. To address those two problems, we propose adding a shuffling stage to the Flink Iceberg streaming writer. The shuffling stage can intelligently group data via bin packing or range partition. This can reduce the number of concurrent files that every task writes. It can also improve data clustering. In this talk, we will explain the motivations in details and dive into the design of the shuffling stage. We will also share the evaluation results that demonstrate the effectiveness of smart shuffling.
by
Gang Ye & Steven Wu
Distributed Databases Deconstructed: CockroachDB, TiDB and YugaByte DBYugabyteDB
Slides for Amey Banarse's, Principal Data Architect at Yugabyte, "Distributed Databases Deconstructed: CockroachDB, TiDB and YugaByte DB" webinar recorded on Oct 30, 2019 at 11 AM Pacific.
Playback here: https://vimeo.com/369929255
Building Robust ETL Pipelines with Apache SparkDatabricks
Stable and robust ETL pipelines are a critical component of the data infrastructure of modern enterprises. ETL pipelines ingest data from a variety of sources and must handle incorrect, incomplete or inconsistent records and produce curated, consistent data for consumption by downstream applications. In this talk, we’ll take a deep dive into the technical details of how Apache Spark “reads” data and discuss how Spark 2.2’s flexible APIs; support for a wide variety of datasources; state of art Tungsten execution engine; and the ability to provide diagnostic feedback to users, making it a robust framework for building end-to-end ETL pipelines.
This talk will tell the story of an analytics use case database from a non-OLAP and ACID-compliant RDBMS (MySQL) perspective.
I will cover the basics of the Clickhouse database Sample Clickhouse installation in a lab environment.
We are configuring Clickhouse for essential operations.
We will load the sample data set and monitor it.
We will query and visualize the results.
This talk will also base on how Kubernetes can help Clickhouse implementation via an operator.
Conclusions will include Do's and Don't of this emerging technology. Best practices and some advice around ingesting and analyzing terabytes of data efficiently.
Flink Forward San Francisco 2022.
Resource Elasticity is a frequently requested feature in Apache Flink: Users want to be able to easily adjust their clusters to changing workloads for resource efficiency and cost saving reasons. In Flink 1.13, the initial implementation of Reactive Mode was introduced, later releases added more improvements to make the feature production ready. In this talk, we’ll explain scenarios to deploy Reactive Mode to various environments to achieve autoscaling and resource elasticity. We’ll discuss the constraints to consider when planning to use this feature, and also potential improvements from the Flink roadmap. For those interested in the internals of Flink, we’ll also briefly explain how the feature is implemented, and if time permits, conclude with a short demo.
by
Robert Metzger
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...Databricks
Spark SQL is a highly scalable and efficient relational processing engine with ease-to-use APIs and mid-query fault tolerance. It is a core module of Apache Spark. Spark SQL can process, integrate and analyze the data from diverse data sources (e.g., Hive, Cassandra, Kafka and Oracle) and file formats (e.g., Parquet, ORC, CSV, and JSON). This talk will dive into the technical details of SparkSQL spanning the entire lifecycle of a query execution. The audience will get a deeper understanding of Spark SQL and understand how to tune Spark SQL performance.
Kafka Connect: Real-time Data Integration at Scale with Apache Kafka, Ewen Ch...confluent
Many companies are adopting Apache Kafka to power their data pipelines, including LinkedIn, Netflix, and Airbnb. Kafka’s ability to handle high throughput real-time data makes it a perfect fit for solving the data integration problem, acting as the common buffer for all your data and bridging the gap between streaming and batch systems.
However, building a data pipeline around Kafka today can be challenging because it requires combining a wide variety of tools to collect data from disparate data systems. One tool streams updates from your database to Kafka, another imports logs, and yet another exports to HDFS. As a result, building a data pipeline can take significant engineering effort and has high operational overhead because all these different tools require ongoing monitoring and maintenance. Additionally, some of the tools are simply a poor fit for the job: the fragmented nature of the data integration tools ecosystem lead to creative but misguided solutions such as misusing stream processing frameworks for data integration purposes.
We describe the design and implementation of Kafka Connect, Kafka’s new tool for scalable, fault-tolerant data import and export. First we’ll discuss some existing tools in the space and why they fall short when applied to data integration at large scale. Next, we will explore Kafka Connect’s design and how it compares to systems with similar goals, discussing key design decisions that trade off between ease of use for connector developers, operational complexity, and reuse of existing connectors. Finally, we’ll discuss how standardizing on Kafka Connect can ultimately lead to simplifying your entire data pipeline, making ETL into your data warehouse and enabling stream processing applications as simple as adding another Kafka connector.
eventbrite_kafka_summit_event_logo_v3-035858-edited.png
The Parquet Format and Performance Optimization OpportunitiesDatabricks
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
Dennis Wittekind, Confluent, Senior Customer Success Engineer
Perhaps you have heard of Kafka Connect and think it would be a great fit in your application's architecture, but you like to know how things work before you propose them to your team? Perhaps you know enough Connect to be dangerous, but you haven't had the time to really understand all the moving pieces? This meetup talk is for you! We'll briefly introduce Connect to the uninitiated, and then jump in to underlying concepts and considerations you should make when running Connect in production! We'll even run a live demo! What could go wrong!?
https://www.meetup.com/Saint-Louis-Kafka-meetup-group/events/272687113/
How to build a streaming Lakehouse with Flink, Kafka, and HudiFlink Forward
Flink Forward San Francisco 2022.
With a real-time processing engine like Flink and a transactional storage layer like Hudi, it has never been easier to build end-to-end low-latency data platforms connecting sources like Kafka to data lake storage. Come learn how to blend Lakehouse architectural patterns with real-time processing pipelines with Flink and Hudi. We will dive deep on how Flink can leverage the newest features of Hudi like multi-modal indexing that dramatically improves query and write performance, data skipping that reduces the query latency by 10x for large datasets, and many more innovations unique to Flink and Hudi.
by
Ethan Guo & Kyle Weller
Scalable and High available Distributed File System Metadata Service Using gR...Alluxio, Inc.
Alluxio Community Office Hour
Apr 7, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speaker: Bin Fan
Alluxio (alluxio.io) is an open-source data orchestration system that provides a single namespace federating multiple external distributed storage systems. It is critical for Alluxio to be able to store and serve the metadata of all files and directories from all mounted external storage both at scale and at speed.
This talk shares our design, implementation, and optimization of Alluxio metadata service (master node) to address the scalability challenges. Particularly, we will focus on how to apply and combine techniques including tiered metadata storage (based on off-heap KV store RocksDB), fine-grained file system inode tree locking scheme, embedded state-replicate machine (based on RAFT), exploration and performance tuning in the correct RPC frameworks (thrift vs gRPC) and etc. As a result of the combined above techniques, Alluxio 2.0 is able to store at least 1 billion files with a significantly reduced memory requirement, serving 3000 workers and 30000 clients concurrently.
In this Office Hour, we will go over how to:
- Metadata storage challenges
- How to combine different open source technologies as building blocks
- The design, implementation, and optimization of Alluxio metadata service
Alluxio - Scalable Filesystem Metadata ServicesAlluxio, Inc.
This talk was presented by Alluxio's top contributor and PMC Maintainer Calvin Jia at the Alluxio bay area Meetup.
This talk shares our design, implementation and optimization of Alluxio metadata service to address the scalability challenges, focusing on how to apply and combine techniques including tiered metadata storage (based on off-heap KV store RocksDB), fine-grained file system inode tree locking scheme, embedded state-replicate machine (based on RAFT), exploration and performance tuning in the correct RPC frameworks (thrift vs gRPC) and etc.
Building a Distributed File System for the Cloud-Native EraAlluxio, Inc.
Big Data Bellevue Meetup
May 19, 2022
For more Alluxio events: https://alluxio.io/events/
Speaker: Bin Fan (Founding Engineer & VP of Open Source, Alluxio)
Today, data engineering in modern enterprises has become increasingly more complex and resource-consuming, particularly because (1) the rich amount of organizational data is often distributed across data centers, cloud regions, or even cloud providers, and (2) the complexity of the big data stack has been quickly increasing over the past few years with an explosion in big-data analytics and machine-learning engines (like MapReduce, Hive, Spark, Presto, Tensorflow, PyTorch to name a few).
To address these challenges, it is critical to provide a single and logical namespace to federate different storage services, on-prem or cloud-native, to abstract away the data heterogeneity, while providing data locality to improve the computation performance. [Bin Fan] will share his observation and lessons learned in designing, architecting, and implementing such a system – Alluxio open-source project — since 2015.
Alluxio originated from UC Berkeley AMPLab (used to be called Tachyon) and was initially proposed as a daemon service to enable Spark to share RDDs across jobs for performance and fault tolerance. Today, it has become a general-purpose, high-performance, and highly available distributed file system to provide generic data service to abstract away complexity in data and I/O. Many companies and organizations today like Uber, Meta, Tencent, Tiktok, Shopee are using Alluxio in production, as a building block in their data platform to create a data abstraction and access layer. We will talk about the journey of this open source project, especially in its design challenges in tiered metadata storage (based on RocksDB), embedded state-replicate machine (based on RAFT) for HA, and evolution in RPC framework (based on gRPC) and etc.
Optimizing Latency-sensitive queries for Presto at Facebook: A Collaboration ...Alluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Optimizing Latency-sensitive queries for Presto at Facebook: A Collaboration between Presto & Alluxio
Ke Wang, Software Engineer (Facebook)
Bin Fan, Founding Engineer, VP Of Open Source (Alluxio)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Optimizing Latency-Sensitive Queries for Presto at Facebook: A Collaboration ...Alluxio, Inc.
Alluxio Global Online Meetup
May 7, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Rohit Jain, Facebook
Yutian "James" Sun, Facebook
Bin Fan, Alluxio
For many latency-sensitive SQL workloads, Presto is often bound by retrieving distant data. In this talk, Rohit Jain, James Sun from Facebook and Bin Fan from Alluxio will introduce their teams’ collaboration on adding a local on-SSD Alluxio cache inside Presto workers to improve unsatisfied Presto latency.
This talk will focus on:
- Insights of the Presto workloads at Facebook w.r.t. cache effectiveness
- API and internals of the Alluxio local cache, from design trade-offs (e.g. caching granularity, concurrency level and etc) to performance optimizations.
- Initial performance analysis and timeline to deliver this feature for general Presto users.
- Discussion on our future work to optimize cache performance with deeper integration with Presto
Introduction to Big Data and NoSQL.
This presentation was given to the Master DBA course at John Bryce Education in Israel.
Work is based on presentations by Michael Naumov, Baruch Osoveskiy, Bill Graham and Ronen Fidel.
Sizing your Content Databases: Understanding the LimitsRandy Williams
Did you know that Microsoft now supports content databases up 4TB and beyond? Hang on though—before you design or adjust your information and service architectures, there are a number of assumptions, caveats and trade-off choices you must understand. We'll discuss these and how database size affects performance, content recovery, and day-to-day administration tasks. We'll then look at various techniques to help you scale out your storage tier. We close the session by sharing the very latest guidance on employing using RBS (Remote BLOB Storage) in your environments.
We Provide Hadoop training institute in Hyderabad and Bangalore with corporate training by 12+ Experience faculty.
Real-time industry experts from MNCs
Resume Preparation by expert Professionals
Lab exercises
Interview Preparation
Experts advice
HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Big Data Architecture Workshop - Vahid Amiridatastack
Big Data Architecture Workshop
This slide is about big data tools, thecnologies and layers that can be used in enterprise solutions.
TopHPC Conference
2019
AI/ML Infra Meetup | ML explainability in MichelangeloAlluxio, Inc.
AI/ML Infra Meetup
May. 23, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Eric Wang (Software Engineer, @Uber)
Uber has numerous deep learning models, most of which are highly complex with many layers and a vast number of features. Understanding how these models work is challenging and demands significant resources to experiment with various training algorithms and feature sets. With ML explainability, the ML team aims to bring transparency to these models, helping to clarify their predictions and behavior. This transparency also assists the operations and legal teams in explaining the reasons behind specific prediction outcomes.
In this talk, Eric Wang will discuss the methods Uber used for explaining deep learning models and how we integrated these methods into the Uber AI Michelangelo ecosystem to support offline explaining.
AI/ML Infra Meetup | Reducing Prefill for LLM Serving in RAGAlluxio, Inc.
AI/ML Infra Meetup
May. 23, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Junchen Jiang (Assistant Professor of Computer Science, @University of Chicago)
Prefill in LLM inference is known to be resource-intensive, especially for long LLM inputs. While better scheduling can mitigate prefill’s impact, it would be fundamentally better to avoid (most of) prefill. This talk introduces our preliminary effort towards drastically minimizing prefill delay for LLM inputs that naturally reuse text chunks, such as in retrieval-augmented generation. While keeping the KV cache of all text chunks in memory is difficult, we show that it is possible to store them on cheaper yet slower storage. By improving the loading process of the reused KV caches, we can still significantly speed up prefill delay while maintaining the same generation quality.
AI/ML Infra Meetup | Perspective on Deep Learning FrameworkAlluxio, Inc.
AI/ML Infra Meetup
May. 23, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Triston Cao (Senior Deep Learning Software Engineering Manager, @NVIDIA)
From Caffe to MXNet, to PyTorch, and more, Xiande Cao, Senior Deep Learning Software Engineer Manager, will share his perspective on the evolution of deep learning frameworks.
AI/ML Infra Meetup | Improve Speed and GPU Utilization for Model Training & S...Alluxio, Inc.
AI/ML Infra Meetup
May. 23, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Lu Qiu (Data & AI Platform Tech Lead, @Alluxio)
- Siyuan Sheng (Senior Software Engineer, @Alluxio)
Speed and efficiency are two requirements for the underlying infrastructure for machine learning model development. Data access can bottleneck end-to-end machine learning pipelines as training data volume grows and when large model files are more commonly used for serving. For instance, data loading can constitute nearly 80% of the total model training time, resulting in less than 30% GPU utilization. Also, loading large model files for deployment to production can be slow because of slow network or storage read operations. These challenges are prevalent when using popular frameworks like PyTorch, Ray, or HuggingFace, paired with cloud object storage solutions like S3 or GCS, or downloading models from the HuggingFace model hub.
In this presentation, Lu and Siyuan will offer comprehensive insights into improving speed and GPU utilization for model training and serving. You will learn:
- The data loading challenges hindering GPU utilization
- The reference architecture for running PyTorch and Ray jobs while reading data from S3, with benchmark results of training ResNet50 and BERT
- Real-world examples of boosting model performance and GPU utilization through optimized data access
Alluxio Monthly Webinar | Simplify Data Access for AI in Multi-CloudAlluxio, Inc.
Alluxio Monthly Webinar
May. 14, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- ChanChan Mao (Developer Advocate, Alluxio)
- Bin Fan (VP of Technology, Alluxio)
Running AI/ML workloads in different clouds present unique challenges. The key to a manageable multi-cloud architecture is the ability to seamlessly access data across environments with high performance and low cost.
This webinar is designed for data platform engineers, data infra engineers, data engineers, and ML engineers who work with multiple data sources in hybrid or multi-cloud environments. Chanchan and Bin will guide the audience through using Alluxio to greatly simplify data access and make model training and serving more efficient in these environments.
You will learn:
- How to access data in multi-region, hybrid, and multi-cloud like accessing a local file system
- How to run PyTorch to read datasets and write checkpoints to remote storage with Alluxio as the distributed data access layer
- Real-world examples and insights from tech giants like Uber, AliPay and more
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed DataAlluxio, Inc.
Alluxio Monthly Webinar
Apr. 23, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- ChanChan Mao (Developer Advocate, Alluxio)
- Shawn Sun (Tech Lead of Cloud Native, Alluxio)
Cloud-native model training jobs require fast data access to achieve shorter training cycles. Accessing data can be challenging when your datasets are distributed across different regions and clouds. Additionally, as GPUs remain scarce and expensive resources, it becomes more common to set up remote training clusters from where data resides. This multi-region/cloud scenario introduces the challenges of losing data locality, resulting in operational overhead, latency and expensive cloud costs.
In the third webinar of the multi-cloud webinar series, Chanchan and Shawn dive deep into:
- The data locality challenges in the multi-region/cloud ML pipeline
- Using a cloud-native distributed caching system to overcome these challenges
- The architecture and integration of PyTorch/Ray+Alluxio+S3 using POSIX or RESTful APIs
- Live demo with ResNet and BERT benchmark results showing performance gains and cost savings analysis
Optimizing Data Access for Analytics And AI with AlluxioAlluxio, Inc.
Alluxio x Tobiko - ETL Happy Hour
April 16, 2024
For more Alluxio events: https://alluxio.io/events/
Speaker:
Lucy Ge (Staff Software Engineer @ Alluxio)
In this presentation, Lucy Ge will discuss the data access challenges in the data pipeline and how to optimize the speed and costs of analytics and AI workloads.
Speed Up Presto at Uber with Alluxio CachingAlluxio, Inc.
Alluxio x Tobiko - ETL Happy Hour
April 16, 2024
For more Alluxio events: https://alluxio.io/events/
Speaker:
Chen Liang (Staff Software Engineer @ Uber)
In this presentation, Chen Liang will share the design and implementation of the Alluxio-Presto local cache to reduce query latency.
Correctly Loading Incremental Data at ScaleAlluxio, Inc.
Alluxio x Tobiko - ETL Happy Hour
April 16, 2024
For more Alluxio events: https://alluxio.io/events/
Speaker:
Toby Mao (CTO @ Tobiko Data)
Writing efficient and correct incremental pipelines is challenging. Data practitioners who take on this challenge are viewed as performing an "advanced" function, which discourages broader teams from adopting incremental loads. In this lightning talk, CTO of Tobiko Data, Toby Mao, will demystify incremental loading data at scale.
Big Data Bellevue Meetup | Enhancing Python Data Loading in the Cloud for AI/MLAlluxio, Inc.
Big Data Bellevue Meetup
March 21, 2024
For more Alluxio events: https://alluxio.io/events/
Speakers:
Bin Fan (VP of Open Source, Alluxio)
In this presentation, Bin Fan (VP of Open Source @ Alluxio) will address a critical challenge of optimizing data loading for distributed Python applications within AI/ML workloads in the cloud, focusing on popular frameworks like Ray and Hugging Face. Integration of Alluxio’s distributed caching for Python applications is accomplished using the fsspec interface, thus greatly improving data access speeds. This is particularly useful in machine learning workflows, where repeated data reloading across slow, unstable or congested networks can severely affect GPU efficiency and escalate operational costs.
Attendees can look forward to practical, hands-on demonstrations showcasing the tangible benefits of Alluxio’s caching mechanism across various real-world scenarios. These demos will highlight the enhancements in data efficiency and overall performance of data-intensive Python applications. This presentation is tailored for developers and data scientists eager to optimize their AI/ML workloads. Discover strategies to accelerate your data processing tasks, making them not only faster but also more cost-efficient.
Alluxio Monthly Webinar | Why a Multi-Cloud Strategy Matters for Your AI Plat...Alluxio, Inc.
Alluxio Monthly Webinar
Feb. 27, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Tarik Bennett (Senior Solutions Engineer, Alluxio)
As GenAI and AI continue to transform businesses, scaling these workloads requires optimized underlying infrastructure. A multi-cloud architecture allows organizations to leverage different cloud services to meet diverse workload demands while maximizing efficiency, reducing costs, and avoiding vendor lock-in. However, achieving a multi-cloud vision can be challenging.
In this webinar, Tarik will share how an agonistic data layer, like Alluxio, allows you to embrace the separation of storage from compute and simplify the adoption of multi-cloud for AI.
- Learn why leveraging multiple cloud providers is critical for balancing performance, scalability, and cost of your AI platform
- Discover how an agnostic data layer like Alluxio provides seamless data access in multi-cloud that bridges storage and compute without data replication
- Gain insights into real-world examples and best practices for deploying AI across on-prem, hybrid, and multi-cloud environments
Alluxio Monthly Webinar | Five Disruptive Trends that Every Data & AI Leader...Alluxio, Inc.
Alluxio Monthly Webinar
Jan. 30, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Kevin Petrie (VP of Research, Eckerson Group)
- Omid Razavi (SVP of Customer Success, Alluxio)
2024 is gearing up to be an impactful year for AI and analytics. Join us on January 30, as Kevin Petrie (VP of Research at Eckerson Group) and Omid Razavi (SVP of Customer Success at Alluxio) share key trends that data and AI leaders should know. This event will efficiently guide you with market data and expert insights to drive successful business outcomes.
- Assess current and future trends in data and AI with industry experts
- Discover valuable insights and practical recommendations
- Learn best practices to make your enterprise data more accessible for both analytics and AI applications
Data Infra Meetup | FIFO Queues are All You Need for Cache EvictionAlluxio, Inc.
Data Infra Meetup
Jan. 25, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Juncheng Yang(Ph.D Candidate, @CMU)
As a cache eviction algorithm, FIFO has a lot of attractive properties, such as simplicity, speed, scalability, and flash-friendliness. The most prominent criticism of FIFO is its low efficiency (high miss ratio). In this talk, I will describe a simple, scalable FIFO-based algorithm with three static queues (S3-FIFO). Evaluated on 6594 cache traces from 14 datasets, we show that S3- FIFO has lower miss ratios than state-of-the-art algorithms across traces. Moreover, S3-FIFO’s efficiency is robust — it has the lowest mean miss ratio on 10 of the 14 datasets. FIFO queues enable S3-FIFO to achieve good scalability with 6× higher throughput compared to optimized LRU at 16 threads. Our insight is that most objects in skewed workloads will only be accessed once in a short window, so it is critical to evict them early (also called quick demotion). The key of S3-FIFO is a small FIFO queue that filters out most objects from entering the main cache, which provides a guaranteed demotion speed and high demotion precision.
Data Infra Meetup | Accelerate Your Trino/Presto Queries - Gain the Alluxio EdgeAlluxio, Inc.
Data Infra Meetup
Jan. 25, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Jingwen Ouyang (Product Manager, @Alluxio)
In this session, Jingwen presents an overview of using Alluxio Edge caching to accelerate Trino or Presto queries. She offers practical best practices for using distributed caching with compute engines. In addition, this session also features insights from real-world examples.
Data Infra Meetup | Accelerate Distributed PyTorch/Ray Workloads in the CloudAlluxio, Inc.
Data Infra Meetup
Jan. 25, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Siyuan Sheng (Senior Software Engineer, @Alluxio)
- Chunxu Tang (Research Scientist, @Alluxio)
In this session, cloud optimization specialists Chunxu and Siyuan break down the challenges and present a fresh architecture designed to optimize I/O across the data pipeline, ensuring GPUs function at peak performance. The integrated solution of PyTorch/Ray + Alluxio + S3 offers a promising way forward, and the speakers delve deep into its practical applications. Attendees will not only gain theoretical insights but will also be treated to hands-on instructions and demonstrations of deploying this cutting-edge architecture in Kubernetes, specifically tailored for Tensorflow/PyTorch/Ray workloads in the public cloud.
Data Infra Meetup | ByteDance's Native Parquet ReaderAlluxio, Inc.
Data Infra Meetup
Jan. 25, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Shengxuan Liu (Software Engineer, @ByteDance)
Shengxuan Liu from ByteDance presents the new ByteDance’s native Parquet Reader. The talk covers the architecture and key features of the Reader, and how the new Reader is able to facilitate data processing efficiency.
Data Infra Meetup | Uber's Data Storage EvolutionAlluxio, Inc.
Data Infra Meetup
Jan. 25, 2024
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Jing Zhao (Principal Engineer, @Uber)
Uber builds one of the biggest data lakes in the industry, which stores exabytes of data. In this talk, we will introduce the evolution of our data storage architecture, and delve into multiple key initiatives during the past several years.
Specifically, we will introduce:
- Our on-prem HDFS cluster scalability challenges and how we solved them
- Our efficiency optimizations that significantly reduced the storage overhead and unit cost without compromising reliability and performance
- The challenges we are facing during the ongoing Cloud migration and our solutions
Alluxio Monthly Webinar | Why NFS/NAS on Object Storage May Not Solve Your AI...Alluxio, Inc.
Alluxio Monthly Webinar
Nov. 15, 2023
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Tarik Bennett (Senior Solutions Engineer)
- Beinan Wang (Senior Staff Engineer & Architect)
Many companies are working with development architectures for AI platforms but have concerns about efficiency at scale as data volumes increase. They use centralized cloud data lakes, like S3, to store training data for AI platforms. However, GPU shortages add more complications. Storage and compute can be separate, or even remote, making data loading slow and expensive:
1) Optimizing a developmental setup can include manual copies, which are slow and error-prone
2) Directly transferring data across regions or from cloud to on-premises can incur expensive egress fees
This webinar covers solutions to improve data loading for model training. You will learn:
- The data loading challenges with distributed infrastructure
- Typical solutions, including NFS/NAS on object storage, and why they are not the best options
- Common architectures that can improve data loading and cost efficiency
- Using Alluxio to accelerate model training and reduce costs
AI Infra Day | Accelerate Your Model Training and Serving with Distributed Ca...Alluxio, Inc.
AI Infra Day
Oct. 25, 2023
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Adit Madan (Director of Product Management, @Alluxio)
In this session, Adit Madan, Director of Product Management at Alluxio, presents an overview of using distributed caching to accelerate model training and serving. He explores the requirements of data access patterns in the ML pipeline and offers practical best practices for using distributed caching in the cloud. This session features insights from real-world examples, such as AliPay, Zhihu, and more.
AI Infra Day | The AI Infra in the Generative AI EraAlluxio, Inc.
AI Infra Day
Oct. 25, 2023
Organized by Alluxio
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Bin Fan (Cheif Architect, VP of Open Source, @Alluxio)
As the AI landscape rapidly evolves, the advancements in generative AI technologies, such as ChatGPT, are driving a need for a robust AI infra stack. This opening keynote will explore the key trends of the AI infra stack in the generative AI era.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Looking for a reliable mobile app development company in Noida? Look no further than Drona Infotech. We specialize in creating customized apps for your business needs.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
2. ● Release Manager for Alluxio 2.0.0
● Contributor since Tachyon 0.4 (2012)
● Founding Engineer @ Alluxio
About Me
Calvin Jia
3. Alluxio Overview
• Open source data orchestration
• Commonly used for data analytics such as OLAP on Hadoop
• Deployed at Huya, Two Sigma, Tencent, and many others
• Largest deployments of over 1000 nodes
7. Alluxio Master
• Responsible for storing and serving metadata in Alluxio
• Alluxio Metadata consists of files and blocks
• Main data structure is the Filesystem Tree
• The namespace for files in Alluxio
• Can include mounts of other file system namespaces
• The size of the tree can be very large!
9. Metadata Storage Challenges
• Storing the raw metadata becomes a problem with a large number of
files
• On average, each file takes 1KB of on-heap storage
• 1 billion files would take 1 TB of heap space!
• A typical JVM runs with < 64GB of heap space
• GC becomes a big problem when using larger heaps
10. Metadata Serving Challenges
• File operations (ie. getStatus, create) need to be fast
• On heap data structures excel in this case
• Operations need to be optimized for high concurrency
• Generally many readers and few writers
14. Tiered Metadata Storage = 1 Billion Files
14
Alluxio Master
Local Disk
RocksDB (Embedded)
● Inode Tree
● Block Map
● Worker Block Locations
On Heap
● Inode Cache
● Mount Table
● Locks
15. Working with RocksDB
• Abstract the metadata storage layer
• Redesign the data structure representation of the Filesystem Tree
• Each inode is represented by a numerical ID
• Edge table maps <ID,childname> to <ID of child> Ex: <1foo, 2>
• Inode table maps <ID> to <Metadata blob of inode> Ex: <2, proto>
• Two table solution provides good performance for common
operations
• One lookup for listing by using prefix scan
• Path depth lookups for tree traversal
• Constant number of inserts for updates/deletes/creates
16. Example RocksDB Operations
• To create a file, /s3/data/june.txt:
• Look up <rootID, s3> in the edge table to get <s3ID>
• Look up <s3ID, data> in the edge table to get <dataID>
• Look up <dataID> in the inode table to get <dataID metadata>
• Update <dataID, dataID metadata> in the inode table
• Put <june.txtID, june.txt metadata> in the inode table
• Put <dataId, june.txt> in the edge table
• To list children of /:
• Prefix lookup of <rootId> in the edge table to get all <childID>s
• Look up each <childID> in the inode table to get <child metadata>
17. Effects of the Inode Cache
• Generally can store up to 10M inodes
• Caching top levels of the Filesystem Tree greatly speeds up read
performance
• 20-50% performance loss when addressing a filesystem tree that does not
mostly fit into memory
• Writes can be buffered in the cache and are asynchronously flushed
to RocksDB
• No requirement for durability - that is handled by the journal
18. Additional & Future Work
• Fast startup time through using RocksDB checkpoints
• More sophisticated cache management policies
19. Conclusion
• RocksDB enables us to leverage offheap storage
• Scales our raw metadata storage by an order of magnitude, allowing
us to address over 1 billion files
• Available in Alluxio 2.0 - Released June 27th 2019!