From Tanel Poder's Troubleshooting Complex Performance Issues series - an example of Oracle SEG$ internal segment contention due to some direct path insert activity.
The document provides an overview of using Automatic Workload Repository (AWR) for memory analysis in an Oracle database. It discusses various memory structures like the database buffer cache, shared pool, and process memory. It outlines signs of memory issues and describes analyzing the top waits, load profile, instance efficiency, SQL areas, and other AWR report sections to identify and address performance problems related to memory configuration and usage.
This is a recording of my Advanced Oracle Troubleshooting seminar preparation session - where I showed how I set up my command line environment and some of the main performance scripts I use!
DB Time, Average Active Sessions, and ASH Math - Oracle performance fundamentalsJohn Beresniewicz
RMOUG 2020 abstract:
This session will cover core concepts for Oracle performance analysis first introduced in Oracle 10g and forming the backbone of many features in the Diagnostic and Tuning packs. The presentation will cover the theoretical basis and meaning of these concepts, as well as illustrate how they are fundamental to many user-facing features in both the database itself and Enterprise Manager.
This is the presentation on ASH that I did with Graham Wood at RMOUG 2014 and that represents the final best effort to capture essential and advanced ASH content as started in a presentation Uri Shaft and I gave at a small conference in Denmark sometime in 2012 perhaps. The presentation is also available publicly through the RMOUG website, so I felt at liberty to post it myself here. If it disappears it would likely be because I have been asked to remove it by Oracle.
Troubleshooting Complex Oracle Performance Problems with Tanel PoderTanel Poder
The document describes troubleshooting a performance issue involving parallel data loads into a data warehouse. It is determined that the slowness is due to recursive locking and buffer busy waits occurring during inserts into the SEG$ table as new segments are created by parallel CREATE TABLE AS SELECT statements. This is causing a nested locking ping-pong effect between the cache, transaction, and I/O layers as sessions repeatedly acquire and release locks and buffers.
Oracle RAC 19c: Best Practices and Secret InternalsAnil Nair
Oracle Real Application Clusters 19c provides best practices and new features for upgrading to Oracle 19c. It discusses upgrading Oracle RAC to Linux 7 with minimal downtime using node draining and relocation techniques. Oracle 19c allows for upgrading the Grid Infrastructure management repository and patching faster using a new Oracle home. The presentation also covers new resource modeling for PDBs in Oracle 19c and improved Clusterware diagnostics.
Exadata and the Oracle Optimizer: The Untold StoryEnkitec
The document discusses how the Oracle optimizer can select suboptimal plans on Exadata due to not properly accounting for the faster speeds of full table scans enabled by Exadata's offloading capabilities and storage compression. It explains how updating statistics to reflect Exadata's multi-block read capabilities and gathering new system statistics can help the optimizer generate better plans that leverage Exadata's performance features.
This document provides an overview of Automatic Workload Repository (AWR) and Active Session History (ASH) reports in Oracle Database. It discusses the various reports available in AWR and ASH, how to generate and interpret them. Key sections include explanations of the AWR reports, using ASH reports to identify specific database issues, and techniques for querying ASH data directly for detailed analysis. The document concludes with examples of using SQL to generate graphs of ASH data from the command line.
Learn from the author of SQLTXPLAIN the fundamentals of SQL Tuning: 1) Diagnostics Collection; 2) Root Cause Analysis (RCA); and 3) Remediation.
SQL Tuning is a complex and intimidating area of knowledge, and it requires years of frequent practice to master it. Nevertheless, there are some concepts and practices that are fundamental to succeed. From basic understanding of the Cost-based Optimizer (CBO) and the Execution Plans, to more advance topics such as Plan Stability and the caveats of using SQL Profiles and SQL Plan Baselines, this session is full of advice and experience sharing. Learn what works and what doesn't when it comes to SQL Tuning.
Participants of this session will also learn about several free tools (besides SQLTXPLAIN) that can be used to diagnose a SQL statement performing poorly, and some others to improve Execution Plan Stability.
Either if your are a novice DBA, or an experienced DBA or Developer, there will be something new for you on this session. And if this is your first encounter with SQL Tuning, at least you will learn the basic concepts and steps to succeed in your endeavor.
The document discusses tuning Oracle GoldenGate performance, including available tools for monitoring replication lag and throughput. It presents a case study examining lag times of over 1 hour 30 minutes for a replication configuration and uses tools like the Streams Performance Advisor and lag reports to identify potential bottlenecks. Recommendations are provided for configuration changes and monitoring to improve replication performance.
This document provides an overview of the Automatic Workload Repository (AWR) and Active Session History (ASH) features in Oracle Database 12c. It discusses how AWR and ASH work, how to access and interpret their reports through the Oracle Enterprise Manager console and command line interface. Specific sections cover parsing AWR reports, querying ASH data directly, and using features like the SQL monitor to diagnose performance issues.
This document provides a summary of a presentation on Oracle Real Application Clusters (RAC) integration with Exadata, Oracle Data Guard, and In-Memory Database. It discusses how Oracle RAC performance has been optimized on Exadata platforms through features like fast node death detection, cache fusion optimizations, ASM optimizations, and integration with Exadata infrastructure. The presentation agenda indicates it will cover these RAC optimizations as well as integration with Oracle Data Guard and the In-Memory database option.
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.
This document provides an overview and interpretation of the Automatic Workload Repository (AWR) report in Oracle database. Some key points:
- AWR collects snapshots of database metrics and performance data every 60 minutes by default and retains them for 7 days. This data is used by tools like ADDM for self-management and diagnosing issues.
- The top timed waits in the AWR report usually indicate where to focus tuning efforts. Common waits include I/O waits, buffer busy waits, and enqueue waits.
- Other useful AWR metrics include parse/execute ratios, wait event distributions, and top activities to identify bottlenecks like parsing overhead, locking issues, or inefficient SQL.
The document discusses various Oracle performance monitoring tools including Oracle Enterprise Manager (OEM), Automatic Workload Repository (AWR), Automatic Database Diagnostic Monitor (ADDM), Active Session History (ASH), and eDB360. It provides overviews of each tool and examples of using AWR, ADDM, ASH and eDB360 for performance analysis through demos. The conclusions recommend OEM as the primary tool and how the other tools like AWR, ADDM and ASH complement it for deeper performance insights.
Understanding my database through SQL*Plus using the free tool eDB360Carlos Sierra
This session introduces eDB360 - a free tool that is executed from SQL*Plus and generates a set of reports providing a 360-degree view of an Oracle database; all without installing anything on the database.
If using Oracle Enterprise Manager (OEM) is off-limits for you or your team, and you can only access the database thorough a SQL*Plus connection with no direct access to the database server, then this tool is a perfect fit to provide you with a broad overview of the database configuration, performance, top SQL and much more. You only need a SQL*Plus account with read access to the data dictionary, and common Oracle licenses like the Diagnostics or the Tuning Pack.
Typical uses of this eDB360 tool include: databases health-checks, performance assessments, pre or post upgrade verifications, snapshots of the environment for later use, compare between two similar environments, documenting the state of a database when taking ownership of it, etc.
Once you learn how to use eDB360 and get to appreciate its value, you may want to execute this tool on all your databases on a regular basis, so you can keep track of things for long periods of time. This tool is becoming part of a large collection of goodies many DBAs use today.
During this session you will learn the basics about the free eDB360 tool, plus some cool tricks. The target audience is: DBAs, developers and consultants (some managers could also benefit).
Understanding oracle rac internals part 2 - slidesMohamed Farouk
This document discusses Oracle Real Application Clusters (RAC) internals, specifically focusing on client connectivity and node membership. It provides details on how clients connect to a RAC database, including connect time load balancing, connect time and runtime connection failover. It also describes the key processes that manage node membership in Oracle Clusterware, including CSSD and how it uses network heartbeats and voting disks to monitor nodes and remove failed nodes from the cluster.
Performance Tuning With Oracle ASH and AWR. Part 1 How And Whatudaymoogala
The document discusses various techniques for identifying and analyzing SQL performance issues in an Oracle database, including gathering diagnostic data from AWR reports, ASH reports, SQL execution plans, and real-time SQL monitoring reports. It provides an overview of how to use these tools to understand what is causing performance problems by identifying what is slow, quantifying the impact, determining the component involved, and analyzing the root cause.
The document provides an overview of analyzing performance data using the Automatic Workload Repository (AWR) in Oracle databases. It discusses how AWR collects snapshots of data from V$ views over time and stores them in database history views. It highlights some key views used in AWR analysis and factors to consider like snapshot intervals and timestamps. Examples are provided to show how to query AWR views to identify top SQL statements by CPU usage and analyze performance metrics trends over time.
Part1 of SQL Tuning Workshop - Understanding the OptimizerMaria Colgan
Part 1 of a 5 part SQL Tuning workshop, This presentation covers the history of the Oracle Optimizer and explains the first thing the Optimizer does when it receives a SQL statements, which is to transform the SQL statement in order to open up additional access paths.
This is a high level presentation I delivered at BIWA Summit. It's just some high level thoughts related to today's NoSQL and Hadoop SQL engines (not deeply technical).
This document discusses connecting Hadoop and Oracle databases. It introduces the author Tanel Poder and his expertise in databases and big data. It then covers tools like Sqoop that can be used to load data between Hadoop and Oracle databases. It also discusses using query offloading to query Hadoop data directly from Oracle as if it were in an Oracle database.
Oracle Exadata Performance: Latest Improvements and Less Known FeaturesTanel Poder
This document discusses recent improvements to Oracle Exadata performance, including improved SQL monitoring in Oracle 12c, enhancements to storage indexes and flash caching, and additional metrics available in AWR. It provides details on new execution plan line level metrics in SQL monitoring reports and metrics for storage cell components now visible in AWR. The post outlines various flash cache features and behavior in earlier Oracle releases.
Tanel Poder Oracle Scripts and Tools (2010)Tanel Poder
Tanel Poder's Oracle Performance and Troubleshooting Scripts & Tools presentation initially presented at Hotsos Symposium Training Day back in year 2010
Tanel Poder - Troubleshooting Complex Oracle Performance Issues - Part 1Tanel Poder
The document describes troubleshooting a complex performance issue in an Oracle database. Key details:
- The problem was sporadic extreme slowness of the Oracle database and server lasting 1-20 minutes.
- Initial AWR reports and OS metrics showed a spike at 18:10 with CPU usage at 66.89%, confirming a problem occurred then.
- Further investigation using additional metrics was needed to fully understand the root cause, as initial diagnostics did not provide enough context about this brief problem period.
Modern Linux Performance Tools for Application TroubleshootingTanel Poder
Modern Linux Performance Tools for Application Troubleshooting.
Mostly demos and focused on application/process troubleshooting, not systemwide summaries.
GNW01: In-Memory Processing for DatabasesTanel Poder
This document discusses in-memory execution for databases. It begins with introductions and background on the author. It then discusses how databases can offload data to memory to improve query performance 2-24x by analyzing storage use and access patterns. It covers concepts like how RAM access is now the performance bottleneck and how CPU cache-friendly data structures are needed. It shows examples measuring performance differences when scanning data in memory versus disk. Finally, it discusses future directions like more integrated storage and memory and new data formats optimized for CPU caches.
Oracle LOB Internals and Performance TuningTanel Poder
The document discusses a presentation on tuning Oracle LOBs (Large Objects). It covers LOB architecture including inline vs out-of-line storage, LOB locators, inodes, indexes and segments. The presentation agenda includes introduction, storing large content, LOB internals, physical storage planning, caching tuning, loading LOBs, development strategies and temporary LOBs. Examples are provided to illustrate LOB structures like locators, inodes and indexes.
Oracle Latch and Mutex Contention TroubleshootingTanel Poder
This is an intro to latch & mutex contention troubleshooting which I've delivered at Hotsos Symposium, UKOUG Conference etc... It's also the starting point of my Latch & Mutex contention sections in my Advanced Oracle Troubleshooting online seminar - but we go much deeper there :-)
Oracle Database In-Memory Option in ActionTanel Poder
The document discusses Oracle Database In-Memory option and how it improves performance of data retrieval and processing queries. It provides examples of running a simple aggregation query with and without various performance features like In-Memory, vector processing and bloom filters enabled. Enabling these features reduces query elapsed time from 17 seconds to just 3 seconds by minimizing disk I/O and leveraging CPU optimizations like SIMD vector processing.
A presentation divided into eight parts on the 'humble project management toolkit' - a set of tools which helps us to effectively manage projects in the face of uncertainty. In these presentations I describe the 'Planning Fallacy'- why projects always go over budget, over time, and fail to deliver to specification. I introduce two of the main causes of the Planning Fallacy: our cognitive biases or 'thinking errors' and complexity. I outline the 'humble project management toolkit,' describing some of the many approaches to project planning, implementation, and monitoring and evaluation that are stored in the toolkit's six 'compartments':
1. Hesitate to encourage reflection;
2. Understand the project's ecosystem;
3. Manage in alignment with the project's ecosystem;
4. Bring in diverse perspectives;
5. Learn constantly; and
6. Embrace uncertainty
A transaction is a collection of database operations that are reliably and efficiently processed as one unit of work. Transactions must have the ACID properties of atomicity, consistency, isolation, and durability. Serializability is an important concept for transactions - a schedule is serializable if it is equivalent to running the transactions in some serial order. Conflict serializability detects conflicting operations between transactions and enforces a serial-equivalent order.
Oracle uses locking to control concurrent access to tables by multiple users and transactions. There are two types of locks - shared locks which allow multiple users to read data simultaneously, and exclusive locks which prevent other users from accessing the data being written to by a transaction. Oracle applies the minimum level of locking required, such as row-level or page-level locks depending on the SQL statement criteria. Users can also explicitly lock tables or rows using LOCK TABLE or SELECT FOR UPDATE statements.
This presentation discusses several new features in Oracle Database 12c including:
1) Total Recall which allows querying historical data as of a past timestamp using Flashback Archive.
2) Context extension which captures additional context like user, client, and IP address with redo data in Flashback Archive.
3) TRUNCATE TABLE now supports cascading deletes to dependent child tables when referenced keys have ON DELETE CASCADE constraints.
This document outlines Oracle's general product direction for informational purposes only. It does not constitute a legal commitment and should not be relied upon for purchasing decisions. The development, release, and timing of any product features described remains at Oracle's sole discretion.
The document discusses database recovery techniques. It describes ARIES, an algorithm that recovers a database to consistency after a crash in three phases: analysis identifies dirty pages, redo repeats logged actions to restore state, and undo undoes uncommitted transactions. The write-ahead logging protocol forces log writes before data page updates to allow recovery using the log. Checkpointing records dirty pages to reduce redo work during recovery.
Suvendu presents on using Oracle CloneDB for fast database refreshes. CloneDB uses write-on-demand technology to create clones of databases over NFS, allowing full database clones to complete in under 10 minutes. This is significantly faster than traditional methods like EXPDP/IMPDP and saves storage space. A demo is shown and some known issues are discussed, such as needing to add a temporary tablespace. CloneDB works best for creating many targets from a single source and for short-lived testing clones.
The Challenge and Reward of Continuous Improvement and Change Management. Why it pays to be persistent with good ideas. Think outside the box... Eventually people will jump on board.
Locking is a concurrency control mechanism used by databases to control simultaneous access to data by multiple users. There are different types of locks like page, table, and row locks that can be applied at various levels of granularity. Locks prevent inconsistent data access and lost updates by blocking other transactions from modifying the same data that a transaction is currently accessing or modifying until that transaction is complete. The database's lock manager monitors all locks to detect and resolve any lock conflicts or deadlocks that may occur between transactions.
The document discusses several new features and enhancements in Oracle Database 11g Release 1. Key points include:
1) Encrypted tablespaces allow full encryption of data while maintaining functionality like indexing and foreign keys.
2) New caching capabilities improve performance by caching more results and metadata to avoid repeat work.
3) Standby databases have been enhanced and can now be used for more active purposes like development, testing, reporting and backups while still providing zero data loss protection.
The document discusses new features in Oracle Database 11g Release 1. Key points include:
1. Encrypted tablespaces allow encryption of data at the tablespace level while still supporting indexing and queries.
2. New caching capabilities improve performance by caching more results in memory, such as function results and query results.
3. Standby databases have enhanced capabilities and can now be used for more active purposes like development, testing and reporting for increased usability and value.
Ash masters : advanced ash analytics on Oracle Kyle Hailey
The document discusses database performance tuning. It recommends using Active Session History (ASH) and sampling sessions to identify the root causes of performance issues like buffer busy waits. ASH provides key details on sessions, SQL statements, wait events, and durations to understand top resource consumers. Counting rows in ASH approximates time spent and is important for analysis. Sampling sessions in real-time can provide the SQL, objects, and blocking sessions involved in issues like buffer busy waits.
This document discusses using Active Session History (ASH) to analyze and troubleshoot performance issues in an Oracle database. It provides an example of using ASH to identify the top CPU-consuming session over the last 5 minutes. It shows how to group and count ASH data to calculate metrics like average active sessions (AAS) and percentage of time spent on CPU. The document also discusses using ASH to identify top waiting sessions and analyze specific wait events like buffer busy waits.
The document discusses monitoring and tuning Oracle databases on z/OS and z/Linux systems. It provides an overview of using Statspack to diagnose performance issues from high CPU usage, I/O utilization, or memory usage based on timed events, SQL statements, and tablespace I/O statistics. Potential causes and remedies are described for each area that could lead to bad response times.
Oracle Architecture document discusses:
1. The cost of an Oracle Enterprise Edition license is $47,500 per processor.
2. It provides an overview of key Oracle components like the instance, database, listener and cost based optimizer.
3. It demonstrates how to start an Oracle instance, check active processes, mount and open a database, and query it locally and remotely after starting the listener.
Oracle Database performance tuning using oratopSandesh Rao
Oratop is a text-based user interface tool for monitoring basic database operations in real-time. This presentation will go into depth on how to use the tool and some example scenarios. It can be used for both RAC and single-instance databases and in combination with top to get a more holistic view of system performance and identify any bottlenecks.
The document discusses using Automatic Workload Repository (AWR) to analyze IO subsystem performance. It provides examples of AWR reports including foreground and background wait events, operating system statistics, wait histograms. The document recommends using this data to identify IO bottlenecks and guide tuning efforts like optimizing indexes to reduce full table scans.
Oracle Open World Thursday 230 ashmastersKyle Hailey
This document discusses database performance tuning using Oracle's ASH (Active Session History) feature. It provides examples of ASH queries to identify top wait events, long running SQL statements, and sessions consuming the most CPU. It also explains how to use ASH data to diagnose specific problems like buffer busy waits and latch contention by tracking session details over time.
This document discusses scaling natural language processing (NLP) tasks by distributing work across multiple processors and machines. It describes running UIMA pipelines on a local cluster managed by Sun Grid Engine (SGE) to parallelize processing of independent documents. The local cluster, called Colfax, has 6 machines with 48 CPU cores and 96GB RAM that can be utilized through SGE job scripts to split work into arrays processed in parallel.
Oratop is a text-based user interface tool for monitoring basic database operations in real time. This presentation will go into depth on how to use the tool and some example scenarios. It can be used for both RAC as well as single-instance database and can be used in combination with top to get a more holistic view of system performance and identify any bottlenecks
The document outlines common problems and solutions for optimizing performance in Oracle Real Application Clusters (RAC). It discusses RAC fundamentals like architecture and cache fusion. Common problems discussed include lost blocks due to interconnect issues, disk I/O bottlenecks, and expensive queries. Diagnostics tools like AWR and ADDM can identify cluster-wide I/O and query plan issues impacting performance. Configuring the private interconnect, I/O, and addressing bad SQL can help resolve performance problems.
In Memory Database In Action by Tanel Poder and Kerry OsborneEnkitec
The document discusses Oracle Database In-Memory option and how it improves performance of data retrieval and processing queries. It provides examples of running a simple aggregation query with and without various performance features like In-Memory, vector processing and bloom filters enabled. Enabling these features reduces query elapsed time from 17 seconds to just 3 seconds by minimizing disk I/O and leveraging CPU optimizations like SIMD vector processing.
Working in Web Operations means dealing with production systems that in most cases needs to be operational 24×7x365.
To reach 99.99999% uptime, you must fail as little as possible.
This talk will go through a few real-world incidents and failures experienced by our small WebOps team, and outline what we are learning (the hard way), and how we’re trying to improve.
What could possibly go wrong? :-)
Quantifying Container Runtime Performance: OSCON 2017 Open Container DayPhil Estes
A talk given at Open Container Day at O'Reilly's OSCON convention in Austin, Texas on May 9th, 2017. This talk describes an open source project, bucketbench, which can be used to compare performance, stability, and throughput of various container engines. Bucketbench currently supports docker, containerd, and runc, but can be extended to support any container runtime. This work was done in response to performance investigations by the Apache OpenWhisk team in using containers as the execution vehicle for functions in their "Functions-as-a-Service" runtime. Find out more about bucketbench here: https://github.com/estesp/bucketbench
What do data center operators need to know when deploying Hadoop in the Data Center? Multi-tenancy, network topology, workload types, and myriad other factors affect the way applications run and perform in the data center. Understanding performance characteristics of the distributed system is key to not only optimize for Hadoop, but allows Hadoop to seamlessly operate side-by-side existing applications.
The document discusses optimization of Real Application Clusters (RAC) in Oracle 12c. It provides background on the author and outlines common root causes of RAC performance issues such as CPU/memory starvation, network issues, and excessive dynamic remastering. The document then presents golden rules for RAC diagnostics including avoiding focusing only on top wait events, eliminating infrastructure issues, identifying problem instances, examining both send and receive side metrics, and using histograms. Specific techniques are described for analyzing wait events like gc buffer busy.
The document is a presentation about new features in PostgreSQL 9.6. It discusses several major new features including parallel queries, avoiding VACUUM on all-frozen pages using freeze maps, monitoring the progress of VACUUM, phrase full text search, multiple synchronous replication, remote_apply synchronous commit, and improved capabilities of the postgres_fdw extension including pushing down sorts, joins, updates and deletes to remote servers.
Oratop is a tool that provides dynamic, near real-time monitoring of an Oracle database. It displays information on the database, instances, wait events, sessions/processes, and SQL. It can be run against RAC or non-RAC databases and provides both command line and interactive modes. In interactive mode, various options can be toggled using keyboard keys to customize the displayed information.
Pythian is a global leader in database administration and consulting services. The document discusses the speaker's first 100 days of experience with an Oracle Exadata database machine. It provides an overview of Exadata components and features like Hybrid Columnar Compression and Smart Scan, which offloads processing from database servers to storage cells.
Similar to Troubleshooting Complex Performance issues - Oracle SEG$ contention (20)
In this session, we explored setting up Playwright, an end-to-end testing tool for simulating browser interactions and running TestBox tests. Participants learned to configure Playwright for applications, simulate user interactions to stress-test forms, and handle scenarios like taking screenshots, recording sessions, capturing Chrome dev tools traces, testing login failures, and managing broken JavaScript. The session also covered using Playwright with non-ColdBox sites, providing practical insights into enhancing testing capabilities.
In this session, we explored how the cbfs module empowers developers to abstract and manage file systems seamlessly across their lifecycle. From local development to S3 deployment and customized media providers requiring authentication, cbfs offers flexible solutions. We discussed how cbfs simplifies file handling with enhanced workflow efficiency compared to native methods, along with practical tips to accelerate complex file operations in your projects.
How to debug ColdFusion Applications using “ColdFusion Builder extension for ...Ortus Solutions, Corp
Unlock the secrets of seamless ColdFusion error troubleshooting! Join us to explore the potent capabilities of Visual Studio Code (VS Code) and ColdFusion Builder (CF Builder) in debugging. This hands-on session guides you through practical techniques tailored for local setups, ensuring a smooth and efficient development experience.
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Ansys Mechanical enables you to solve complex structural engineering problems and make better, faster design decisions. With the finite element analysis (FEA) solvers available in the suite, you can customize and automate solutions for your structural mechanics problems and parameterize them to analyze multiple design scenarios. Ansys Mechanical is a dynamic tool that has a complete range of analysis tools.
Lots of bloggers are using Google AdSense now. It’s getting really popular. With AdSense, bloggers can make money by showing ads on their websites. Read this important article written by the experienced designers of the best website designing company in Delhi –
Break data silos with real-time connectivity using Confluent Cloud Connectorsconfluent
Connectors integrate Apache Kafka® with external data systems, enabling you to move away from a brittle spaghetti architecture to one that is more streamlined, secure, and future-proof. However, if your team still spends multiple dev cycles building and managing connectors using just open source Kafka Connect, it’s time to consider a faster and cost-effective alternative.
Are you wondering how to migrate to the Cloud? At the ITB session, we addressed the challenge of managing multiple ColdFusion licenses and AWS EC2 instances. Discover how you can consolidate with just one EC2 instance capable of running over 50 apps using CommandBox ColdFusion. This solution supports both ColdFusion flavors and includes cb-websites, a GoLang binary for managing CommandBox websites.
Join me for an insightful journey into task scheduling within the ColdBox framework. In this session, we explored how to effortlessly create and manage scheduled tasks directly in your code, enhancing control and efficiency in applications and modules. Attendees experienced a user-friendly dashboard for seamless task management and monitoring. Whether you're experienced with ColdBox or new to it, this session provided practical knowledge and tips to streamline your development workflow.
Explore the latest in ColdBox Debugger v4.2.0, featuring the Hyper Collector for HTTP/S request tracking, Lucee SQL Collector for query profiling, and Heap Dump Support for memory leak debugging. Enhancements like the revamped Request Dock and improved SQL/JSON formatting streamline debugging for optimal ColdBox application performance and stability. Ideal for developers familiar with ColdBox, this session focuses on leveraging advanced debugging tools to enhance development efficiency.
Discover Passkeys, the next evolution in secure login methods that eliminate traditional password vulnerabilities. Learn about the CBSecurity Passkeys module's installation, configuration, and integration into your application to enhance security.
In this session, we discussed the critical need for comprehensive backups across all aspects of our industry—from code and databases to webservers, file servers, and network configurations. Emphasizing the importance of proactive measures, attendees were urged to ensure their backup systems were tested through restoration processes. The session underscored the risk of discovering backup issues only during crises, highlighting the necessity of verifying backup integrity through restoration tests.
2. gluent.com 2
Intro:
About
me
• Tanel
Põder
• Oracle
Database
Performance
geek
(18+
years)
• Exadata
Performance
geek
• Linux
Performance
geek
• Hadoop
Performance
geek
• CEO
&
co-‐founder:
Expert
Oracle
Exadata
book
(2nd edition
is
out
now!)
Instant
promotion
3. gluent.com 3
Gluent:
All
Enterprise
Data
Available
in
Hadoop!
GluentHadoop
Gluent
MSSQL
Tera-‐
data
IBM
DB2
Big
Data
Sources
Oracle
App
X
App
Y
App
Z
8. gluent.com 8
A
Data
Warehouse
data
loading/preparation
• A
large
Exadata
/
RAC
/
Oracle
11.2.0.3
reporting
environment
• Lots
of
parallel
CTAS
and
direct
path
loads
• High
concurrency,
high
parallelism
• Throughput
bad,
all
kinds
of
waits
showing
up:
• Other,
Cluster,
Configuration,
Application,
Concurrency,
User
I/O,
CPU
12. gluent.com 12
Wait
event
explanation
• buffer
busy
waits
• Buffer
is
physically
available
in
local
instance,
but
is
pinned
by
some
other
local
session
(in
incompatible
mode)
• gc
buffer
busy
acquire
• Someone
has
already
requested
the
remote
block
into
local
instance
• gc
buffer
busy
release
• Block
is
local,
but
has
to
be
shipped
out
to
a
remote
instance
first
(as
someone
in
the
remote
instance
had
requested
it
first)
• enq:
TX
-‐ allocate
ITL
entry
• Can't
change
block
as
all
the
block's
ITL
entries
are
held
by
others
• Can't
dynamically
add
more
ITLs
(no
space
in
block)
15. gluent.com 15
Which
object?
• Translate
file#,
block#
into
segment
names/numbers:
• Assumes
the
blocks
are
in
buffer
cache
SQL> SELECT objd data_object_id, COUNT(*)
FROM v$bh
WHERE file#=1
AND block# IN
( 279634,279635,279629,279632,279638,279636,279613,279662,
279628,279608,279653,279627,279642,279637,279643,279631,
279646,279622,279582,279649
)
GROUP BY objd ORDER BY 2 DESC;
DATA_OBJECT_ID COUNT(*)
-------------- ----------
8 113 All
blocks
belong
to
a
segment
with
data_object_id
=
8
16. gluent.com 16
What
is
segment
#8?
• Look
up
the
object
names:
• Using
DBA_OBJECTS.DATA_OBJECT_ID
-‐>
OBJECT_ID
SQL> @doid 8
object_id owner object_name O_PARTITION object_type
--------- --------- ----------------- ------------- -----------
8 SYS C_FILE#_BLOCK# CLUSTER
14 SYS SEG$ TABLE
13 SYS UET$ TABLE
This
segment
#8
is
a
cluster
which
contains
SEG$
(DBA_SEGMENTS)
and
UET$
(DBA_EXTENTS),
but
UET$
isn't
used
anymore
thanks
to
LMT
tablespaces.
That
leaves
SEG$
17. gluent.com 17
Write
contention
on
SEG$?
• SEG$ is
modified
when:
1. A
new
segment
is
created
(table,
index,
partition,
etc)
2. An
existing
segment
extends
3. A
segment
is
dropped
/
moved
4. Parallel
direct
path
loads
(CTAS)
can
also
create
many
temporary
segments
(one
per
PX
slave)
• …and
merge
these
into
final
segment
in
the
end
of
loading
More
about
this
later…
19. gluent.com 19
AWR
1
• No
CPU
starvation
evident
(checked
all
RAC
nodes)
Top 5 Timed Foreground Events
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Event Waits Time(s) (ms) time Wait Class
---------------------------- ----------- -------- ----- ------ ----------
DB CPU 165,199 37.0
gc buffer busy acquire 1,260,128 68,399 54 15.3 Cluster
enq: TX - allocate ITL entry 354,496 40,583 114 9.1 Configurat
direct path write temp 4,632,946 37,455 8 8.4 User I/O
gc buffer busy release 213,750 22,683 106 5.1 Cluster
Host CPU (CPUs: 160 Cores: 80 Sockets: 8)
~~~~~~~~ Load Average
Begin End %User %System %WIO %Idle
--------- --------- --------- --------- --------- ---------
5.20 42.76 26.0 3.3 0.0 69.8
20. gluent.com 20
AWR
2
• AWR
also
listed
a
SEG$
insert
as
a
top
SQL:
Elapsed Elapsed Time
Time (s) Executions per Exec (s) %Total %CPU %IO SQL Id
----------- ------------- ------------- ------ ----- ----- -------------
100,669.1 4,444 22.65 22.6 .5 .0 g7mt7ptq286u7
insert into seg$ (file#,block#,type#,ts#,blocks,extents,minexts,maxexts,
extsize,extpct,user#,iniexts,lists,groups,cachehint,hwmincr, spare1,
scanhint, bitmapranges) values (:1,:2,:3,:4,:5,:6,:7,:8,:9,:10,:11,:12,
:13,:14,:15,:16,DECODE(:17,0,NULL,:17),:18,:19)
SNAP_ID NODE SQL_ID EXECS AVG_ETIME AVG_LIO AVG_PIO
------- ---- ------------- ------ ---------- -------- --------
24263 3 g7mt7ptq286u7 552 70.227 880.6 .0
• AWR
on
another
node
(same
insert
into
seq$):
Super-‐slow
single
row
inserts
into
SEG$
???
24. gluent.com 24
Checkpoint
1. Lots
of
parallel
CTAS
statements,
seem
to
wait
for
various
RAC
gc
buffer
busy
events
and
enq:
TX
– ITL
contention
2. CTAS
statements
create
new
segments,
new
segments
cause
inserts
into
SEG$
3. AWR
and
SQL
Trace
report
super-‐long
elapsed
times
for
single-‐row
SEG$
inserts
4. It's
actually
the
recursive
SEG$
inserts
that
wait
for
the
gc
buffer
busy and
enq:
TX
– ITL
contention
events
26. gluent.com 26
Multiple
layers
of
locking
&
coherency
ITL
2ITL
1
data2data1ccLH c2c1
ITL
4ITL
3
KCBH
-‐ block
common
header
KTBBH
-‐ transaction
common
header
KDBH
-‐ Data
header
Data block
Row
Level
0:
VOS/service
layer
-‐ Cache
buffers
chains
latch
Level
1:
Cache
layer
Buffer
pin
-‐ local
pin
-‐ global
cache
pin
Level
2:
Transaction
layer
-‐ ITL
entry
-‐ row
lock
byte
You
must
pin
(and
retrieve)
a
buffer
before
you
can
change
or
examine
it
What
if
you
pin
a
buffer
and
find
all
its
ITL
slots
busy?
(with
no
space
to
add
mode)
The
session
will
release
the
pin
and
start
waiting
for
enq:
TX
– ITL
contention event
And
re-‐get/pin
again
when
TX
–
ITL
wait
is
over!
28. gluent.com 28
OBJ
#9
and
#14
• Look
up
the
object
names:
• Using
DBA_OBJECTS.DATA_OBJECT_ID
-‐>
OBJECT_ID
SQL> @oid 9,14
owner object_name object_type
--------------- --------------------- ------------------
SYS I_FILE#_BLOCK# INDEX
SYS SEG$ TABLE
The
object
#9
is
a
cluster
index
for
cluster
C_FILE#_BLOCK#
(oid #8
on
a
previous
slide
SQL> @bclass 4
CLASS UNDO_SEGMENT_ID
------------------ ---------------
segment header
Bclass
#4
is
a
segment
header
block
that
also
stores
freelist
information
30. gluent.com 30
Conclusion
from
gathered
evidence
1. Lots
of
concurrent
+
highly
parallel
CTAS
statements
2. Each
PX
slave
created
its
own
temporary
segments
(in
spikes
when
PX
query
started
up)
3. Spikes
of
concurrent SEG$
inserts
simultaneously
on
4
RAC
nodes
4. SEG$
cluster
blocks
out
of
ITL
entries
5. This
caused
global
cache/TX
locking
trashing
– long
loops
of
unsuccessful
attempts
to
pin
and
insert
into
SEG$
blocks
31. gluent.com 31
Also
worth
noting
1. Resource
manager
a
possible
factor
• Plenty
of
resmgr:cpu quantum waits
• Lock
holders,
buffer
pin
holders
might
have
gone
to
sleep
– while
holding
the
pins/locks
under
contention
1. Freelist-‐based
block
space
allocation
• SEG$
is
a
SYS-‐owned
freelist-‐managed
index
cluster
• Many
cluster
table
blocks
walked
through
when
searching
for
space
2. ASH
doesn't
always
show
recursive
SQL
IDs
• Attributes
waits
to
parent
(top
level)
statement
SQL
ID
instead
• ASH
p1,
p2,
p3,
current_obj#
columns
are
useful
for
drilldown
32. gluent.com 32
How
to
fix
it?
• Do
it
less!
• What?
• Do
not
insert/update
SEG$
entries
so
frequently!
• How?
• Do
not
allow
parallel
direct
load
insert
to
create
a
temporary
loading
segment
for
each
slave (and
inserted
partition)!
• How?
• Make
sure
that
High
Water
Mark
Brokering
is
used!
• One
temporary
segment
-‐ thus
one
SEG$
insert/update/delete
-‐per
query
(and
inserted
partition)
=
much
less
SEG$
contention.
• More
in
following
slides
…
33. gluent.com 33
Why
is
High-‐Water-‐Mark
Brokering
needed?
• A
historical
problem
with
large
uniform
extent
sizes:
1. Each
data
loading
PX
slave
allocated
its
own
extents
(to
its
private
temporary
segment)
when
loading
data
2. When
the
load
was
completed
the
private
temporary
segments
were
merged
to
one
final
table
segment
3. The
last extent
of
each
private
segment
possibly
ended
up
not
fully
used
• Some
were
almost
full,
some
almost
empty
– half-‐empty
on
average
4. Wastage
=
0.5
x
extent_size x
PX_slaves
• The
more
PX
slaves,
the
more
wastage
• The
bigger
extent,
the
bigger
wastage
• References:
• https://blogs.oracle.com/datawarehousing/entry/parallel_load_uniform
_or_autoallocate
• http://www.oracle.com/technetwork/database/bi-‐
datawarehousing/twpdwbestpractices-‐for-‐loading-‐11g-‐404400.pdf
Problem
Solved
by
using
High-‐Water-‐Mark
Brokering!
But…
34. gluent.com 34
Parallel
Data
Loading
and
Large
Extents
– space
wastage?
SQL> @seg sales_parallel_ctas
SEG_MB OWNER SEGMENT_NAME SEG_TABLESPACE_NAME
------- ------- ------------------------- --------------------
9472 TANEL SALES_PARALLEL_CTAS TANEL_DEMO_LARGE
8896 TANEL SALES_PARALLEL_CTAS_FIX TANEL_DEMO_LARGE
SQL>SELECT * FROM TABLE(space_tools.get_space_usage('TANEL', 'SALES_PARALLEL_CTAS','TABLE'));
---------------------------------------------------------------------------------------
Full blocks /MB 1134669 8865
Unformatted blocks/MB 0 0
Free Space 0-25% 0 0
Free Space 25-50% 0 0
Free Space 50-75% 0 0
Free Space 75-100% 72963 570
SQL>SELECT * FROM TABLE(space_tools.get_space_usage('TANEL', 'SALES_PARALLEL_CTAS_FIX’,'TABLE'))
----------------------------------------------------------------------------------------
Full blocks /MB 1134669 8865
Unformatted blocks/MB 0 0
Free Space 0-25% 0 0
Free Space 25-50% 0 0
Free Space 50-75% 0 0
Free Space 75-100% 0 0
An
identical
table
PX
CTAS
in
the
same
tablespace
with
64MB
uniform
extent
(upper
table
6.4%
bigger)
570
MB
worth
of
blocks
unused
in
the
segment?
PX
DOP
16
x
64
*
~0.5
=
512MB
Same
table
loaded
with
INSERT
/*+
APPEND
*/
PX
DOP
16.
No
empty
blocks
35. gluent.com 35
Parallel
Data
Loading
and
Large
Extents
– HWM
brokering
• Instead
of
each
PX
slave
working
on
their
own
separate
segment…
1. Allocate
and
extend
only
one
segment
2. Slaves
allocate
space
within
the
single
segment
as
needed
3. No
“holes”
in
tables,
no
space
wastage
problem
anymore!
4. Serialized
via
the
HV
-‐ Direct
Loader
High
Water
Mark enqueue
• Parameter:
• _insert_enable_hwm_brokered =
true
(default)
• “during
parallel
inserts
high
water
marks
are
brokered”
• Problem:
• In
Oracle
11.2
(including
11.2.0.4)
this
only
works
with
INSERT
APPENDs
• …and
not
for
CTAS
nor
ALTER
TABLE
MOVE
• (in
one
case
it
worked
for
CTAS,
only
if
the
target
table
was
partitioned)
36. gluent.com 36
Parallel
Data
Loading
and
Large
Extents
– HWM
brokering
SQL> @fix 6941515
BUGNO VALUE DESCRIPTION IS_DEFAULT
------- ------ ------------------------------------------------------------ ----------
6941515 0 use high watermark brokering for insert into single segment 1
SQL> alter session set "_fix_control"='6941515:ON';
Session altered.
• Actually
this
is
a
bug,
and
Oracle
has
fixed
it
long
time
ago
• But
bugfix 6941515
is
not
enabled
by
default!
• After
enabling
the
bug-‐fix,
parallel
CTAS
and
parallel
ALTER
TABLE
MOVE
also
use
HWM
brokering…
• Unfortunately
it
didn’t
work
with
ALTER
TABLE
MOVE
if
moving
the
whole
table
if
the
table
was
partitioned.
DBMS_SPACE
/
space_tools helps
to
measure
this!
Thanks
to
Alex
Fatkulin for
spotting
this!
38. gluent.com 38
Case
Study:
Data
Loading
Performance
– Example
1
• Parallel
Create
Table
As
Select
from
Hadoop
to
Exadata
• Buffer
busy
waits
dominating
the
response
time
profile
(SQL
Monitoring)
http://blog.tanelpoder.com/2013/11/06/diagnosing-‐buffer-‐busy-‐waits-‐with-‐the-‐
ash_wait_chains-‐sql-‐script-‐v0-‐2/
39. gluent.com 39
Data
Loading
Performance:
ashtop.sql
SQL> @ash/ashtop session_state,event,p2text,p2,p3text,p3 sql_id='3rtbs9vqukc71'
"timestamp'2013-10-05 01:00:00'" "timestamp'2013-10-05 03:00:00'"
%This SESSION EVENT P2TEXT P2 P3TEXT P3
------ ------- --------------------------------- --------- -------- -------- -------
57% WAITING buffer busy waits block# 2 class# 13
31% ON CPU file# 0 size 524288
1% WAITING external table read file# 0 size 524288
1% ON CPU block# 2 class# 13
0% ON CPU consumer 12573 0
0% WAITING cell smart file creation 0 0
0% WAITING DFS lock handle id1 3 id2 2
0% ON CPU file# 41 size 41
0% WAITING cell single block physical read diskhash# 4695794 bytes 8192
0% WAITING control file parallel write block# 1 requests 2
0% WAITING control file parallel write block# 41 requests 2
0% WAITING change tracking file synchronous blocks 1 0
0% WAITING control file parallel write block# 42 requests 2
0% WAITING db file single write block# 1 blocks 1
0% ON CPU 0 0
• Break
the
(buffer
busy)
wait
events
down
by
block#/class#
block
#2
?
40. gluent.com 40
Case
Study:
Data
Loading
Performance
– Example
2
• Lots
of
serial
sessions
doing
single
row
inserts
– on
multiple
RAC
nodes
• “buffer
busy
waits”
and
“gc
buffer
busy
acquire”
waits
• file#
=
6
• block#
=
2
• class#
=
13
SQL> @bclass 13
CLASS UNDO_SEGMENT_ID
------------------ ---------------
file header block
SQL> select file#, block#, status
from v$bh where class# = 13;
FILE# BLOCK# STATUS
---------- ---------- ----------
5 2 xcur
4 2 xcur
...
Block dump from disk:
buffer tsn: 7 rdba: 0x00000002 (1024/2)
scn: 0x0000.010b6f9b seq: 0x02 flg: 0x04 tail:
0x6f9b1d02
frmt: 0x02 chkval: 0xd587 type: 0x1d=KTFB
Bitmapped File Space Header
Hex dump of block: st=0, typ_found=1
A
single
space
allocation
contention
point
per
LMT
file.
Bigfile
tablespaces
have
only
one
file!
42. gluent.com 42
Case
Study:
Parallel
Data
Loading
Performance
• Reduce
demand
on
the
LMT
bitmap
blocks
• By
allocating
bigger
extents
at
a
time
• Use
large
uniform
extents
for
fast-‐growing
tables(paces)
• The
customer
went
with
64MB
uniform
extent
size
• The
autoallocate extent
management
is
suboptimal
for
very
large
segments
• As
there’d
be
1
LMT
space
management
bit
per
64kB
regardless
of
your
INITIAL
extent
size
at
the
segment
level
• The
_partition_large_extents=
TRUE
doesn’t
change
this
either
• Large
uniform
extents
are
better
for
data
loading
and
scanning!