RMOUG Training Days 2020 abstract: The Active Session History (ASH) mechanism is a rich source of fine-grained data about database activity, and is the lynchpin for many database performance management features in the Diagnostic and Tuning packs. Many interesting stories about happenings in the database are buried in ASH waiting to be revealed, and data visualization is key to sifting these out from the high dimensionality and volume of ASH data. The session will cover a number of data visualization experiments conducted using a single ASH dump with an emphasis on the iterative process of discovering useful data visualizations.

1. Architect, Enterprise and Cloud Manageability
Oracle America
John Beresniewicz
Data visualization research experiments using ASH data
#ASHviz

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Two decades of Oracle performance visualization
Savant Diagnostic Center for Oracle
• “What’s going on?”
Oracle Enterprise Manager
• Performance Page
• Top Activity
• ASH Analytics
ASH is the mother lode
• System-wide in scope
• Fine grained detail
Background

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Expose ASH secrets using data visualization
Investigate event count estimation in particular
Develop facility with various data viz constructs
Purpose

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ASH dump file
• Raw production data snapshot
RStudio
• R development IDE
tidyverse
• R packages for data science
ggplot
• High quality data visualization
Data and tools
Real data and high quality free
tools enable rapid progress

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4 csv files from 4-node RAC ASH dump
• Oracle 10.2 database circa 2004
Production OLTP workload
V$EVENT_NAME data
• mapping event ID to name and waitclass name
Imported, curated, packaged for re-use
ASH data

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ashDF
• Raw csv ASH dump data from 4 RAC instances
• Curated for analysis
Events
• Split wait events out from ashDF
Core data frames
ashDF <- ashdat::ASHDUMP1
Events <- ashDF %>% filter( TIME_WAITED >0 )

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SAMPLE_TIME: 6 digits after secs
• microsecond time deltas
Almost all columns are Factors
214K sample rows from 4 instances
Added columns:
• MINIT (for minute summaries)
• STATE (cpu, IO, wait)
• STATE_CLASS (cpu or waitclass)
• EST_COUNT (event count estimate)
str(ashDF)

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Full-featured IDE
Integrated with tidyverse
Multiple project file types
• R script
• R Notebook
• R Markdown
• R Presentation
R development: Rstudio

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A Hadley Wickham creation
Collection of convenience packages for R programming
• Data science and visualization
Common philosophy and interface patterns
Designed to work together consistently
Data wrangling: tidyverse
install.packages(“tidyverse”)
dplyr
tibble
forcats
lubridate
ggplot2

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www.tidyverse.org
“The tidyverse is an opinionated collection of R packages
designed for data science. All packages share an underlying
design philosophy, grammar, and data structures.”

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R data pipeline
Samples <- ashDF %>%
# trim
select(INSTANCE_NUMBER, SAMPLE_TIME, SAMPLE_ID) %>%
# compute by instance
group_by(INSTANCE_NUMBER) %>%
# unique samples
distinct(SAMPLE_TIME, SAMPLE_ID) %>%
# get previous sample time
mutate(PREV_TIME = lag(SAMPLE_TIME, order_by = SAMPLE_ID)) %>%
# compute sample time deltas in milliseconds
mutate(DIFF_MSECS = 1000 *
as.numeric(difftime(SAMPLE_TIME, PREV_TIME, units = "secs")))

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Based on Wilkinson’s layered “Grammar of Graphics”
Plot in layers composed of:
• Data (data frame)
• Statistical transform (stat_count)
• Geometric objects (geom_histogram)
• Coordinates, faceting, position
Supports progressive iteration, refinement and composition
Many nice extension packages (gganimate, ggraph)
Visualizing data: ggplot2

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Data Frame
• Essentially a table wrapped into an object with special properties
Aesthetics
• Map data variables to graphical attributes: x, y, color, size
Geoms
• Geometric objects to render using the aesthetics
Faceting
• Subdivide plot into multiple sub-plots (e.g. by instance)
Plot components

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ggplot examples
d4 <- ashDF %>% group_by(MINIT, STATE) %>% summarize(AAS = n()/60)
#
p <- ggplot(d4, aes(x = MINIT, y = AAS, fill = STATE))
#
p + geom_area()
ggplot(data = Events,
aes(x = EVENT_ID, y = log10(TIME_WAITED))) +
geom_boxplot() +
geom_hline(yintercept = c(3,6), color = "red") +
scale_x_discrete(labels=NULL)

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Aesthetics and scales
• Faceting
• Filtering
• Log scales
• Point sizes
• Alpha values
• Zoom
• Colour
Geoms
• Line charts
• Histograms
• Dot plots / bubble charts
• Box-whisker plots
• Density and violin plots
• Polar coordinates
Tidy data
• Pipe
• Mutate
• Filter
• Group_by
Techniques used

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Tidyverse:
• https://www.tidyverse.org
• https://rstudio.com
Details about ASH mechanism:
• ASH Architecture and Advanced Usage
ASHviz article series on LinkedIn:
• https://www.linkedin.com/in/john-beresniewicz-986b0/detail/recent-activity/posts/
Google / Stackoverflow
Resources

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Decide on a target objective
• What is hiding in ASH to reveal?
Imagine visualizations for exposing objective
• Map to geoms
Create data frame for plotting
• Apply stats or mutations to ashDF
Plot data with geom
• Evaluate effectiveness
Tweak plot parameters, change geoms
ASHviz process
Decide
Imagine
CreatePlot
Tweak

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• Active sessions over time
• ASH sampler timing consistency
• Event latency point clouds
• Event latency distributions
• Mapping DB time to sessions and SQL
Investigations

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highlight gallery

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@JBeresniewicz
john.beresniewicz@oracle.com
Thank you