Registers are groups of D flip-flops that store data and are synchronized by a single clock. They allow new data to be loaded in parallel using a load signal. Shift registers can transfer data serially by shifting the stored bits. A universal shift register can operate as a parallel load/shift register or serial-in serial-out register depending on the shift control inputs.
IT Monitoring in the Era of Containers | Luca Deri Founder & Project Lead | ntopInfluxData
Network traffic monitoring tools are traditionally based on the packet paradigm where tools need to analyse each incoming and outgoing packet. As systems are moving towards a micro-service oriented architecture based on containers, the packet paradigm is no longer enough to provide IT visibility as services interact inside a system and not over a network where it is possible to install network sensors. This talk will explain how open source tools designed by ntop on top of InfluxDB allow packet monitoring tools to be complemented with container monitoring and thus implement a lightweight visibility solution for modern IT infrastructures.
This presentation provides an overview of the architecture and technology of TiDB, an open-source distributed NewSQL database, and how it helps Mobike, one of the largest dockless bikeshare platform, scale its infrastructure to achieve hyper-growth.
InfluxDB IOx Tech Talks: A Rusty Introduction to Apache Arrow and How it App...InfluxData
InfluxDB IOx Tech Talks - December 2020
A Rusty Introduction to Apache Arrow and How it Applies to a Time Series Database
This session will start with a tech talk from an InfluxDB IOx team member. This is your chance to interact directly with Influxers who are available to answer your questions about all things InfluxDB IOx and time series — including Paul Dix, Founder and CTO of InfluxData. This event will last about an hour and there will be time for live Q&A.
Testing and Monitoring and Broken Things | Nikki Attea | SensuInfluxData
As a small startup team of developers, release engineering and quality assurance was inherently problematic. To optimize these processes, Sensu implemented a full automated test infrastructure for staging and end-to-end testing, which later became known as the QA Crucible. To increase observability on the overall ecosystem, Nikki started to research the intersection of monitoring and testing in a CI/CD pipeline, and sought to implement existing tooling to further optimize the workflow. By instrumenting JSON test results within a monitoring solution, she discovered a major upside to how tests are run, visualized, responded to, and remediated. Not only can tests be run continuously, but results can also be routed through an event pipeline for data manipulation, visibility and alerting. This pattern allows operators to treat test failures as incidents, and persists test results as metrics in a time-series database for analysis. This technology stack uses RSpec for automated tests, Sensu as an event pipeline, InfluxDB for metric storage, and Grafana for visual dashboards.
IT Monitoring in the Era of Containers | Luca Deri Founder & Project Lead | ntopInfluxData
Network traffic monitoring tools are traditionally based on the packet paradigm where tools need to analyse each incoming and outgoing packet. As systems are moving towards a micro-service oriented architecture based on containers, the packet paradigm is no longer enough to provide IT visibility as services interact inside a system and not over a network where it is possible to install network sensors. This talk will explain how open source tools designed by ntop on top of InfluxDB allow packet monitoring tools to be complemented with container monitoring and thus implement a lightweight visibility solution for modern IT infrastructures.
This presentation provides an overview of the architecture and technology of TiDB, an open-source distributed NewSQL database, and how it helps Mobike, one of the largest dockless bikeshare platform, scale its infrastructure to achieve hyper-growth.
InfluxDB IOx Tech Talks: A Rusty Introduction to Apache Arrow and How it App...InfluxData
InfluxDB IOx Tech Talks - December 2020
A Rusty Introduction to Apache Arrow and How it Applies to a Time Series Database
This session will start with a tech talk from an InfluxDB IOx team member. This is your chance to interact directly with Influxers who are available to answer your questions about all things InfluxDB IOx and time series — including Paul Dix, Founder and CTO of InfluxData. This event will last about an hour and there will be time for live Q&A.
Testing and Monitoring and Broken Things | Nikki Attea | SensuInfluxData
As a small startup team of developers, release engineering and quality assurance was inherently problematic. To optimize these processes, Sensu implemented a full automated test infrastructure for staging and end-to-end testing, which later became known as the QA Crucible. To increase observability on the overall ecosystem, Nikki started to research the intersection of monitoring and testing in a CI/CD pipeline, and sought to implement existing tooling to further optimize the workflow. By instrumenting JSON test results within a monitoring solution, she discovered a major upside to how tests are run, visualized, responded to, and remediated. Not only can tests be run continuously, but results can also be routed through an event pipeline for data manipulation, visibility and alerting. This pattern allows operators to treat test failures as incidents, and persists test results as metrics in a time-series database for analysis. This technology stack uses RSpec for automated tests, Sensu as an event pipeline, InfluxDB for metric storage, and Grafana for visual dashboards.
Optimizing InfluxDB Performance in the Real World | Sam Dillard | InfluxDataInfluxData
Sam will provide practical tips and techniques learned from helping hundreds of customers deploy InfluxDB and InfluxDB Enterprise. This includes hardware and architecture choices, schema design, configuration setup, and running queries.
Introduction to InfluxDB 2.0 & Your First Flux Query by Sonia Gupta, Develope...InfluxData
Bring your laptop ready to go(*) and get started with InfluxDB 2.0 and how to build your first set of dashboards and Flux queries.
* Prerequisites: InfluxDB 2.0 and Telegraf need to be installed on your laptop before the session in order to follow along.
Streaming Sensor Data with Grafana and InfluxDB | Ryan Mckinley | GrafanaInfluxData
In this session, Ryan will preview the new streaming and shared query support in Grafana. He will show how you can visualize high-resolution real-time sensor streams using InfluxDB and Grafana.
Creating and Using the Flux SQL Datasource | Katy Farmer | InfluxData InfluxData
This talk introduces the SQL data source for Flux. It will start with examples of using data from MySQL or Postgres with time series data from InfluxDB. It will then go over the details of how the SQL data source was created.
Register
Serial Input Serial Output
Serial Input Parallel Output
Parallel Input Serial Output
Parallel Input Parallel Output
Flip-flop is a 1 bit memory cell which can be used for storing the digital data. To increase the storage capacity in terms of number of bits, we have to use a group of flip-flop. Such a group of flip-flop is known as a Register. The n-bit register will consist of n number of flip-flop and it is capable of storing an n-bit word.
The binary data in a register can be moved within the register from one flip-flop to another.
This talk will examine the tools, methods and data behind the DDoS attacks that are prevalent in the news headlines and the impacts they can have on companies. I will look at the motivations and rationale that they have and try to share some sort of understanding as to what patterns to be aware of for their own protection.
Change Data Feed is a new feature of Delta Lake on Databricks that is available as a public preview since DBR 8.2. This feature enables a new class of ETL workloads such as incremental table/view maintenance and change auditing that were not possible before. In short, users will now be able to query row level changes across different versions of a Delta table.
In this talk we will dive into how Change Data Feed works under the hood and how to use it with existing ETL jobs to make them more efficient and also go over some new workloads it can enable.
Optimizing InfluxDB Performance in the Real World | Sam Dillard | InfluxDataInfluxData
Sam will provide practical tips and techniques learned from helping hundreds of customers deploy InfluxDB and InfluxDB Enterprise. This includes hardware and architecture choices, schema design, configuration setup, and running queries.
Introduction to InfluxDB 2.0 & Your First Flux Query by Sonia Gupta, Develope...InfluxData
Bring your laptop ready to go(*) and get started with InfluxDB 2.0 and how to build your first set of dashboards and Flux queries.
* Prerequisites: InfluxDB 2.0 and Telegraf need to be installed on your laptop before the session in order to follow along.
Streaming Sensor Data with Grafana and InfluxDB | Ryan Mckinley | GrafanaInfluxData
In this session, Ryan will preview the new streaming and shared query support in Grafana. He will show how you can visualize high-resolution real-time sensor streams using InfluxDB and Grafana.
Creating and Using the Flux SQL Datasource | Katy Farmer | InfluxData InfluxData
This talk introduces the SQL data source for Flux. It will start with examples of using data from MySQL or Postgres with time series data from InfluxDB. It will then go over the details of how the SQL data source was created.
Register
Serial Input Serial Output
Serial Input Parallel Output
Parallel Input Serial Output
Parallel Input Parallel Output
Flip-flop is a 1 bit memory cell which can be used for storing the digital data. To increase the storage capacity in terms of number of bits, we have to use a group of flip-flop. Such a group of flip-flop is known as a Register. The n-bit register will consist of n number of flip-flop and it is capable of storing an n-bit word.
The binary data in a register can be moved within the register from one flip-flop to another.
This talk will examine the tools, methods and data behind the DDoS attacks that are prevalent in the news headlines and the impacts they can have on companies. I will look at the motivations and rationale that they have and try to share some sort of understanding as to what patterns to be aware of for their own protection.
Change Data Feed is a new feature of Delta Lake on Databricks that is available as a public preview since DBR 8.2. This feature enables a new class of ETL workloads such as incremental table/view maintenance and change auditing that were not possible before. In short, users will now be able to query row level changes across different versions of a Delta table.
In this talk we will dive into how Change Data Feed works under the hood and how to use it with existing ETL jobs to make them more efficient and also go over some new workloads it can enable.
4. Registers with Parallel Load
• Control Loading the Register with New Data
D7
D6
D5
D4
D3
D2
D1
D0
R
E
G
I
S
T
E
R
Q7
Q6
Q5
Q4
Q3
Q2
Q1
Q0
LD
0
1
Q(t+1)
Q(t)
D
LD
4 / 28
5. Registers with Parallel Load
• Should we block the “Clock” to keep the “Data”?
D7
D6
D5
D4
D3
D2
D1
D0
R
E
G
I
S
T
E
R
LD
Q7
Q6
Q5
Q4
Q3
Q2
Q1
Q0
I0
D
Q
A0
I1
D
Q
A1
I2
D
Q
A2
I3
D
Q
A3
Delays
the Clock
Load
CLK
5 / 28
6. Registers with Parallel Load
• Circulate the “old data”
I0
I0
MUX
I1 S
Y
D
Q
A0
I1
I0
MUX
I1 S
Y
D
Q
A1
I2
I0
MUX
I1 S
Y
D
Q
A2
I3
I0
MUX
I1 S
Y
D
Q
A3
Load
CLK
6 / 28
7. Shift Registers
• 4-Bit Shift Register
Serial
Input
SI
D
Q
D
Q
D
Q
D
Q
SO
Serial
Output
CLK
7 / 28