The document discusses using Netezza query plans to improve performance. It explains that Netezza generates a query plan detailing the optimal execution path determined by the query optimizer. The query plan shows snippets that read/join data and where they execute. Key points in plans like estimated rows, costs and distributions are examined to identify issues. Common performance problems and steps for analysis are outlined, like generating statistics, changing distributions and rewriting queries.
A look at some of the ways available to deploy Postgres in a Kubernetes cloud environment, either in small scale using simple configurations, or in larger scale using tools such as Helm charts and the Crunchy PostgreSQL Operator. A short introduction to Kubernetes will be given to explain the concepts involved, followed by examples from each deployment method and observations on the key differences.
Overview of HBase cluster replication feature, covering implementation details as well as monitoring tools and tips for troubleshooting and support of Replication deployments.
Hadoop World 2011: Advanced HBase Schema Design - Lars George, ClouderaCloudera, Inc.
"While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second.
This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns. "
Analyzing MySQL Logs with ClickHouse, by Peter ZaitsevAltinity Ltd
This document discusses analyzing MySQL logs with ClickHouse. It describes how ClickHouse is fast, efficient, and easy to use for log analysis. Various options for loading MySQL logs into ClickHouse are presented, including using Logstash, Kafka, or writing your own loader. Specific examples covered include analyzing MySQL audit logs and slow query logs in ClickHouse for troubleshooting and performance insights. The document also briefly mentions using Percona Monitoring and Management for processed log monitoring and Grafana dashboards for ClickHouse.
High level design for replicating HBase bulk loaded data. Detailed design document can be found at https://issues.apache.org/jira/browse/HBASE-13153.
It was presented at Apache HBase Meetup 2015, Bangalore.
Upgrading HDFS to 3.3.0 and deploying RBF in production #LINE_DMYahoo!デベロッパーネットワーク
LINE Developer Meetup #68 - Big Data Platformの発表資料です。HDFSのメジャーバージョンアップとRouter-based Federation(RBF)の適用について紹介しています。イベントページ: https://line.connpass.com/event/188176/
Materialize: a platform for changing dataAltinity Ltd
Frank McSherry, Chief Scientist from Materialize, joins the SF Bay Area ClickHouse meetup to introduce Materialize, which creates real-time materialized views on event streams. Materialize is in the same space, solving similar problems to ClickHouse. It's fun to hear what the neighbors are up to.
Materialize: https://materialize.com
Meetup: https://www.meetup.com/San-Francisco-Bay-Area-ClickHouse-Meetup/events/282872933/
Altinity: https://altinity.com
This document provides an overview of PostgreSQL and instructions for installing and configuring it. It discusses using the initdb command to initialize a PostgreSQL database cluster and create the template1 and postgres databases. It also explains that the template1 database serves as a template that is copied whenever new databases are created.
Operating PostgreSQL at Scale with KubernetesJonathan Katz
The maturation of containerization platforms has changed how people think about creating development environments and has eliminated many inefficiencies for deploying applications. These concept and technologies have made its way into the PostgreSQL ecosystem as well, and tools such as Docker and Kubernetes have enabled teams to run their own “database-as-a-service” on the infrastructure of their choosing.
All this sounds great, but if you are new to the world of containers, it can be very overwhelming to find a place to start. In this talk, which centers around demos, we will see how you can get PostgreSQL up and running in a containerized environment with some advanced sidecars in only a few steps! We will also see how it extends to a larger production environment with Kubernetes, and what the future holds for PostgreSQL in a containerized world.
We will cover the following:
* Why containers are important and what they mean for PostgreSQL
* Create a development environment with PostgreSQL, pgadmin4, monitoring, and more
* How to use Kubernetes to create your own "database-as-a-service"-like PostgreSQL environment
* Trends in the container world and how it will affect PostgreSQL
At the conclusion of the talk, you will understand the fundamentals of how to use container technologies with PostgreSQL and be on your way to running a containerized PostgreSQL environment at scale!
Oracle GoldenGate 18c - REST API ExamplesBobby Curtis
This document provides examples of using RESTful APIs with Oracle GoldenGate 18c. It includes examples of creating, deleting, and listing extracts, replicats, credentials, and distribution paths using cURL commands. It also provides examples of using RESTful APIs within shell scripts to automate administration tasks like adding extracts and replicats.
This document discusses conflict detection and resolution in Oracle GoldenGate 12.3. It provides an overview of Oracle GoldenGate and its architecture. It then covers conflict management in multi-master replication environments, including examples and supported conflict types. It discusses requirements for conflict detection and resolution, such as ensuring full before images and supplemental logging. It also reviews extract, replicat, and exception table parameters used to configure conflict management.
This document discusses tuning HBase and HDFS for performance and correctness. Some key recommendations include:
- Enable HDFS sync on close and sync behind writes for correctness on power failures.
- Tune HBase compaction settings like blockingStoreFiles and compactionThreshold based on whether the workload is read-heavy or write-heavy.
- Size RegionServer machines based on disk size, heap size, and number of cores to optimize for the workload.
- Set client and server RPC chunk sizes like hbase.client.write.buffer to 2MB to maximize network throughput.
- Configure various garbage collection settings in HBase like -Xmn512m and -XX:+UseCMSInit
Low Code Integration with Apache Camel.pdfClaus Ibsen
Design your integration flows using Camel and JBang for a better developer experience, and make it easily production grade using Quarkus.
Claus Ibsen, Apache Camel lead & Senior Principal Software Engineer, Red Hat
Presented by Rags Srinivas, Developer Advocate/Architect at Datastax at Kubernetes Community Days, Washington DC, September 14, 2022.
Cassandra is designed for multi-region
● Partition tolerant
● Each node in the cluster maintains the full topology
● Nodes automatically route traffic to nearby neighbors
● Data is automatically and asynchronously replicated
● The cluster is homogenous
● Any node can service any client request
● Clients can be configured to automatically route traffic to the local datacenter
Kubernetes was not designed for multi-region
● Increased latencies
● The cost is higher consensus request latency from crossing data center boundaries
● Loss of connectivity to ectd could cause outages
● Services should route traffic to nearby endpoints
This document discusses Patroni, an open-source tool for managing high availability PostgreSQL clusters. It describes how Patroni uses a distributed configuration system like Etcd or Zookeeper to provide automated failover for PostgreSQL databases. Key features of Patroni include manual and scheduled failover, synchronous replication, dynamic configuration updates, and integration with backup tools like WAL-E. The document also covers some of the challenges of building automatic failover systems and how Patroni addresses issues like choosing a new master node and reattaching failed nodes.
This document provides an overview of Oracle performance tuning fundamentals. It discusses key concepts like wait events, statistics, CPU utilization, and the importance of understanding the operating system, database, and business needs. It also introduces tools for monitoring performance like AWR, ASH, and dynamic views. The goal is to establish a foundational understanding of Oracle performance concepts and monitoring techniques.
Parallel processing involves executing multiple tasks simultaneously using multiple cores or processors. It can provide performance benefits over serial processing by reducing execution time. When developing parallel applications, developers must identify independent tasks that can be executed concurrently and avoid issues like race conditions and deadlocks. Effective parallelization requires analyzing serial code to find optimization opportunities, designing and implementing concurrent tasks, and testing and tuning to maximize performance gains.
The document provides an overview of the fundamentals of Websphere MQ including:
- The key MQ objects like messages, queues, channels and how they work
- Basic MQ administration tasks like defining, displaying, altering and deleting MQ objects using MQSC commands
- Hands-on exercises are included to demonstrate programming with MQ and administering MQ objects
There are two main types of relational database management systems (RDBMS): row-based and columnar. Row-based systems store all of a row's data contiguously on disk, while columnar systems store each column's data together across all rows. Columnar databases are generally better for read-heavy workloads like data warehousing that involve aggregating or retrieving subsets of columns, whereas row-based databases are better for transactional systems that require updating or retrieving full rows frequently. The optimal choice depends on the specific access patterns and usage of the data.
The document discusses project risk management. It defines project risk as the loss multiplied by the likelihood. Successful project leaders plan thoroughly to understand challenges, anticipate problems, and minimize variation. Project failures can occur if objectives are impossible, deliverables are possible but other objectives are unrealistic, or feasible deliverables and objectives but insufficient planning. Risk management includes qualitative and quantitative risk assessment to understand probability and impact of risks. It is important to document risks, have risk management plans, and regularly review assumptions and risks.
The document discusses HDFS architecture and components. It describes how HDFS uses NameNodes and DataNodes to store and retrieve file data in a distributed manner across clusters. The NameNode manages the file system namespace and regulates access to files by clients. DataNodes store file data in blocks and replicate them for fault tolerance. The document outlines the write and read workflows in HDFS and how NameNodes and DataNodes work together to manage data storage and access.
NENUG Apr14 Talk - data modeling for netezzaBiju Nair
This document discusses considerations for data modeling on Netezza appliances to optimize performance. It recommends distributing data uniformly across snippet processors to maximize parallel processing. When joining tables, the distribution key should match join columns to keep processors independent. Zone maps and clustered tables can reduce data reads from disk. Materialized views on frequently accessed columns further improve performance for single table and join queries.
This document summarizes a presentation about optimizing HBase performance through caching. It discusses how baseline tests showed low cache hit rates and CPU/memory utilization. Reducing the table block size improved cache hits but increased overhead. Adding an off-heap bucket cache to store table data minimized JVM garbage collection latency spikes and improved memory utilization by caching frequently accessed data outside the Java heap. Configuration parameters for the bucket cache are also outlined.
The document discusses Oracle system catalogs which contain metadata about database objects like tables and indexes. System catalogs allow accessing information through views with prefixes like USER, ALL, and DBA. Examples show how to query system catalog views to get information on tables, columns, indexes and views. Query optimization and evaluation are also covered, explaining how queries are parsed, an execution plan is generated, and the least cost plan is chosen.
The document provides an overview of the layers and processes involved in executing a query in Oracle, from when a client connects and sends a query to when the results are returned. It describes the layers of Oracle's architecture, the parsing, optimization, execution plan generation and execution of the query. Key steps include connecting, parsing, optimizing, generating and executing a query plan, updating and committing any changes, and fetching the results.
Implementation of query optimization for reducing run timeAlexander Decker
This document discusses query optimization techniques to improve performance. It proposes performing query optimization at compile-time using histograms of data statistics rather than at run-time. Histograms are used to estimate selectivity of query joins and predicates at compile-time, allowing a query plan to be constructed in advance and executed without run-time optimization. The technique uses a split and merge algorithm to incrementally maintain histograms as data changes. Selectivity estimation with histograms allows join and predicate ordering to be determined at compile-time for query plan generation. Experimental results showed this compile-time optimization approach improved runtime performance over traditional run-time optimization.
This document discusses an issue where the same SQL statement is using two different execution plans on the PRODUCTION database. The SQL is performing an update on the TBL_XXX table. One plan uses a full table scan while the other uses an index range scan on the primary key index TBL_XXX_PK. Statistics show the primary key index has a very high clustering factor, indicating data is scattered across blocks, likely due to row migration from updates. A test case confirms around 200 rows span multiple blocks. Adjusting an optimizer parameter reduces the cost of the index plan, but a better solution is needed to address the underlying data distribution issue causing row migration.
This paper describes how the optimizer uses statistics and determines plans for executing SQL statement. It explains how the 10053 trace file can be used to understand Oracle's decisions on execution plans.
Managing Statistics for Optimal Query PerformanceKaren Morton
Half the battle of writing good SQL is in understanding how the Oracle query optimizer analyzes your code and applies statistics in order to derive the “best” execution plan. The other half of the battle is successfully applying that knowledge to the databases that you manage. The optimizer uses statistics as input to develop query execution plans, and so these statistics are the foundation of good plans. If the statistics supplied aren’t representative of your actual data, you can expect bad plans. However, if the statistics are representative of your data, then the optimizer will probably choose an optimal plan.
Database tuning is the process of optimizing a database to maximize performance. It involves activities like configuring disks, tuning SQL statements, and sizing memory properly. Database performance issues commonly stem from slow physical I/O, excessive CPU usage, or latch contention. Tuning opportunities exist at the level of database design, application code, memory settings, disk I/O, and eliminating contention. Performance monitoring tools like the Automatic Workload Repository and wait events help identify problem areas.
Presto is an open-source distributed SQL query engine for interactive analytics. It uses a connector architecture to query data across different data sources and formats in the same query. Presto's query planning and execution involves scanning data sources, optimizing query plans, distributing queries across workers, and aggregating results. Understanding Presto's query plans helps optimize queries and troubleshoot performance issues.
This document discusses various techniques for optimizing SQL queries in SQL Server, including:
1) Using parameterized queries instead of ad-hoc queries to avoid compilation overhead and improve plan caching.
2) Ensuring optimal ordering of predicates in the WHERE clause and creating appropriate indexes to enable index seeks.
3) Understanding how the query optimizer works by estimating cardinality based on statistics and choosing low-cost execution plans.
4) Avoiding parameter sniffing issues and non-deterministic expressions that prevent accurate cardinality estimation.
5) Using features like the Database Tuning Advisor and query profiling tools to identify optimization opportunities.
1. Kusto (Azure Data Explorer) is a fast and flexible data exploration service for analyzing security and application logs, performance counters, and other streaming data.
2. A Data Engineer's role is evolving to focus more on real-time analysis using Kusto as opposed to traditional SQL. Understanding how to use Kusto's query engine and data ingestion capabilities is key.
3. Techniques like using materialized views, partitioning data, and leader-follower databases can help distribute workloads and improve query performance at scale in Kusto. However, Kusto has limitations around concurrency, memory usage, and result set sizes that need to be considered.
PostgreSQL High-Performance Cheat Sheets contains quick methods to find performance issues.
Summary of the course so that when problems arise, you are able to easily uncover what are the performance bottlenecks.
PHP UK 2020 Tutorial: MySQL Indexes, Histograms And other ways To Speed Up Yo...Dave Stokes
Slow query? Add an index or two! But things are suddenly even slower! Indexes are great tools to speed data lookup but have overhead issues. Histograms don’t have that overhead but may not be suited. And how you lock rows also effects performance. So what do you do to speed up queries smartly?
Predictive performance analysis using sql pattern matchingHoria Berca
This document discusses using SQL pattern matching to analyze database performance metrics over time and identify patterns. It describes how the MATCH_RECOGNIZE clause allows defining patterns using regular expressions to analyze partitioned and ordered metric data. Identifying patterns in metrics like logical reads or CPU load can help predict future performance and optimize resource utilization.
Matt Smiley
This is a basic primer aimed primarily at developers or DBAs new to Postgres. The format is a Q/A style tour with examples, based on common questions and pitfalls. Begin with a quick tour of relevant parts of the postgres catalog, with an aim to answer simple but important questions like:
How many rows does the optimizer think my table has?
When was it last analyzed?
Which other tables also have a column named "foo"?
How often is this index used?
Basic Query Tuning Primer - Pg West 2009mattsmiley
Intro to query tuning in Postgres, for beginners or intermediate software developers. Lists your basic toolkit, common problems, a series of examples. Assumes the audience knows basic SQL but has little or no experience with reading or adjusting execution plans. Accompanies 45-90 minute talk; meant to encourage Q/A.
William Schaffrans Bus Intelligence Portfoliowschaffr
This document provides an overview and examples of the author's work with Microsoft's Business Intelligence Suite, including SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL Server Reporting Services (SSR), Performance Point Server 2007 (PPS), and Microsoft Office SharePoint Server (MOSS). It showcases various packages, data flows, cubes, dimensions, measures, reports, scorecards, and dashboards created by the author using these tools to analyze and report on business data.
This one is about advanced indexing in PostgreSQL. It guides you through basic concepts as well as through advanced techniques to speed up the database.
All important PostgreSQL Index types explained: btree, gin, gist, sp-gist and hashes.
Regular expression indexes and LIKE queries are also covered.
GridSQL is an open source distributed database built on PostgreSQL that allows it to scale horizontally across multiple servers by partitioning and distributing data and queries. It provides significantly improved performance over a single PostgreSQL instance for large datasets and queries by parallelizing processing across nodes. However, it has some limitations compared to PostgreSQL such as lack of support for advanced SQL features, slower transactions, and need for downtime to add nodes.
Brad McGehee's presentation on "How to Interpret Query Execution Plans in SQL Server 2005/2008".
Presented to the San Francisco SQL Server User Group on March 11, 2009.
Similar to Using Netezza Query Plan to Improve Performace (20)
Chef conf-2015-chef-patterns-at-bloomberg-scaleBiju Nair
This document discusses various patterns used at Bloomberg for managing infrastructure at scale using Chef. It describes how dedicated bootstrap servers are used to regularly build clusters in an isolated manner. The use of lightweight VMs for bootstrapping is explained. Techniques for building the bootstrap server, cleaning up configurations and converting it to an admin client are outlined. The document also covers topics like dynamic resource creation, injecting logic into community cookbooks, handling service restarts and implementing pluggable alerts.
This document provides an overview of HBase internals and operations. It discusses how HBase is used at Bloomberg to store over 51 TB of compressed data across billions of reads and writes per day. The document then covers key aspects of HBase including its ordered key-value store architecture, write process, read process, versioning, and ACID compliance. It also discusses HBase deployment configurations including masters, region servers, and Zookeeper coordination.
Kafka is a distributed streaming platform. It uses Zookeeper for coordination between brokers. Producers send data to topics which are divided into partitions. Consumers join consumer groups and are assigned partitions. Brokers elect leaders for each partition and replicate data across in-sync replicas for fault tolerance.
Serving queries at low latency using HBaseBiju Nair
This document discusses how Bloomberg uses HBase to serve billions of queries with millisecond latency. It covers HBase principles like being an ordered key-value store and providing ACID transactions. It also discusses modeling data for HBase, including dealing with data and query skew. Implementation details covered include caching, block size tuning, column families, and compaction. The overall goal is to optimize HBase for Bloomberg's low-latency data storage and retrieval needs.
This document discusses Bloomberg's experience moving to a multi-tenant HBase cluster. It provides an overview of HBase features that support multi-tenancy like namespaces, region server groups, storage quotas, and request throttling. It also summarizes Bloomberg's implementation including creation of namespaces, region server groups, and quotas. Performance results showed region server groups improved data locality and throughput. Overall, the speaker concluded HBase's multi-tenancy story is good but could be improved further with enhancements to features like system table availability and memory quotas.
The document discusses cursors in Apache Phoenix. It describes the need for cursors to support row pagination in queries. It outlines the cursor lifecycle including declaring, opening, fetching rows, and closing a cursor. It presents options for implementing cursors by rewriting queries or wrapping result sets. Challenges with cursors include maintaining data consistency across fetches and optimizing caching. Contributors to cursors in Phoenix are also acknowledged.
This document provides an overview of securing Hadoop applications and clusters. It discusses authentication using Kerberos, authorization using POSIX permissions and HDFS ACLs, encrypting HDFS data at rest, and configuring secure communication between Hadoop services and clients. The principles of least privilege and separating duties are important to apply for a secure Hadoop deployment. Application code may need changes to use Kerberos authentication when accessing Hadoop services.
This document summarizes patterns for building clusters using Chef and providing services on demand. It discusses using node attributes to store service requests, templates to generate configuration, and recipes to start services. Separate roles are used to define services and handle restarts. Pluggable alerts allow defining metrics and alerts. Logic injection techniques allow customizing community cookbooks by intercepting notifications and including custom recipes.
DefCamp_2016_Chemerkin_Yury-publish.pdf - Presentation by Yury Chemerkin at DefCamp 2016 discussing mobile app vulnerabilities, data protection issues, and analysis of security levels across different types of mobile applications.
Finetuning GenAI For Hacking and DefendingPriyanka Aash
Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
"Hands-on development experience using wasm Blazor", Furdak Vladyslav.pptxFwdays
I will share my personal experience of full-time development on wasm Blazor
What difficulties our team faced: life hacks with Blazor app routing, whether it is necessary to write JavaScript, which technology stack and architectural patterns we chose
What conclusions we made and what mistakes we committed
UiPath Community Day Amsterdam: Code, Collaborate, ConnectUiPathCommunity
Welcome to our third live UiPath Community Day Amsterdam! Come join us for a half-day of networking and UiPath Platform deep-dives, for devs and non-devs alike, in the middle of summer ☀.
📕 Agenda:
12:30 Welcome Coffee/Light Lunch ☕
13:00 Event opening speech
Ebert Knol, Managing Partner, Tacstone Technology
Jonathan Smith, UiPath MVP, RPA Lead, Ciphix
Cristina Vidu, Senior Marketing Manager, UiPath Community EMEA
Dion Mes, Principal Sales Engineer, UiPath
13:15 ASML: RPA as Tactical Automation
Tactical robotic process automation for solving short-term challenges, while establishing standard and re-usable interfaces that fit IT's long-term goals and objectives.
Yannic Suurmeijer, System Architect, ASML
13:30 PostNL: an insight into RPA at PostNL
Showcasing the solutions our automations have provided, the challenges we’ve faced, and the best practices we’ve developed to support our logistics operations.
Leonard Renne, RPA Developer, PostNL
13:45 Break (30')
14:15 Breakout Sessions: Round 1
Modern Document Understanding in the cloud platform: AI-driven UiPath Document Understanding
Mike Bos, Senior Automation Developer, Tacstone Technology
Process Orchestration: scale up and have your Robots work in harmony
Jon Smith, UiPath MVP, RPA Lead, Ciphix
UiPath Integration Service: connect applications, leverage prebuilt connectors, and set up customer connectors
Johans Brink, CTO, MvR digital workforce
15:00 Breakout Sessions: Round 2
Automation, and GenAI: practical use cases for value generation
Thomas Janssen, UiPath MVP, Senior Automation Developer, Automation Heroes
Human in the Loop/Action Center
Dion Mes, Principal Sales Engineer @UiPath
Improving development with coded workflows
Idris Janszen, Technical Consultant, Ilionx
15:45 End remarks
16:00 Community fun games, sharing knowledge, drinks, and bites 🍻
Top 12 AI Technology Trends For 2024.pdfMarrie Morris
Technology has become an irreplaceable component of our daily lives. The role of AI in technology revolutionizes our lives for the betterment of the future. In this article, we will learn about the top 12 AI technology trends for 2024.
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...Zilliz
Enterprises have traditionally prioritized data quantity, assuming more is better for AI performance. However, a new reality is setting in: high-quality data, not just volume, is the key. This shift exposes a critical gap – many organizations struggle to understand their existing data and lack effective curation strategies and tools. This talk dives into these data challenges and explores the methods of automating data curation.
It's your unstructured data: How to get your GenAI app to production (and spe...Zilliz
So you've successfully built a GenAI app POC for your company -- now comes the hard part: bringing it to production. Aparavi addresses the challenges of AI projects while addressing data privacy and PII. Our Service for RAG helps AI developers and data scientists to scale their app to 1000s to millions of users using corporate unstructured data. Aparavi’s AI Data Loader cleans, prepares and then loads only the relevant unstructured data for each AI project/app, enabling you to operationalize the creation of GenAI apps easily and accurately while giving you the time to focus on what you really want to do - building a great AI application with useful and relevant context. All within your environment and never having to share private corporate data with anyone - not even Aparavi.