help

help.txt

I need help understanding what each element of a header from a ESXi top output represents. Here is a header:
\\abcd1\Group Cpu(10:drivers)\% Used
ChatGPT said:
Reasoned about ESXi top header elements for 6 seconds
First, it’s worth noting that esxtop (or resxtop) output can come from interactive sessions or batch/CSV exports, and the exact formatting can vary. However, the line you provided,

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\\abcd1\Group Cpu(10:drivers)\% Used
is typically seen when exporting esxtop metrics into CSV or importing those counters into a performance-monitoring tool such as Windows Perfmon. Here’s what each part generally means:

\abcd1
This is the name of the ESXi host (or the system name being monitored). In some CSV/perfmon-like formats, the path to a counter will start with \\hostname (just like Windows PerfMon counters).

Group Cpu(10:drivers)

Group Cpu indicates that the metric is tied to a resource pool or VM group relating to CPU usage. In esxtop/resxtop context, “Group” often refers to a Resource Pool grouping or a world group.
(10:drivers) typically indicates:
10: The World ID (or Resource Pool ID) assigned by the hypervisor. ESXi assigns numeric IDs to each running “world” (e.g., a VM, a system service, or a resource pool).
drivers: The name of that resource pool or group.
So “Cpu(10:drivers)” means “This is the CPU usage for the resource world whose ID is 10 and whose name is drivers.”

% Used
This is the counter or metric that shows the percentage of CPU usage for that resource group. In esxtop CPU terms, %USED is typically the percentage of CPU core cycles used during the measurement interval by that world (or group).

Putting it all together
\\abcd1 — Hostname (the ESXi server).
Group Cpu(10:drivers) — A CPU resource group with ESXi “world” ID 10 named drivers.
% Used — The percentage of CPU time actually used by that group/world over the sample interval.
Thus, the header means: On host “abcd1,” show me the percentage of CPU usage (% Used) for the CPU resource group identified as (10:drivers).







You said:
A host is a particular VM? And it can have multiple CPU resource groups assigned to it?
ChatGPT said:
Reasoned for 6 seconds
Short Answer

In esxtop CSV notation, the “host” (e.g., \\abcd1) is almost always the physical ESXi server itself—not a VM.
Yes, a single ESXi host can have multiple CPU resource groups, which may represent different VMs, resource pools, or system processes. Each group is identified by a “world ID” and a name.
Clarification
1. The “host” is the ESXi server
When you see a path like

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\\abcd1\Group Cpu(10:drivers)\% Used
the portion \\abcd1 is typically the hostname (FQDN or short name) of the ESXi physical server from which esxtop is collecting metrics.

It can look confusing if you’re used to Windows PerfMon counters—which also use the \\hostname\object(instance)\counter format. In this esxtop CSV, \\abcd1 is not referring to a virtual machine but to the physical ESXi host itself.

2. Multiple CPU resource groups on a single host
Within ESXi, many “worlds” (processes, VMs, or resource pools) run on one physical server. Each “world” is assigned a unique numeric World ID. For example:

A VM might appear as Cpu(42:MyVM)
A Resource Pool might appear as Group Cpu(10:drivers)
ESXi system processes also have their own IDs.
Hence, a single ESXi host can show many CPU groups/worlds, each representing a different entity (a VM, a pool, a system service, etc.).

Why you might see something called “drivers”
If your environment is set up with a resource pool named drivers or if the VM was internally referencing a group called drivers, esxtop labels that group accordingly. The 10 is the World ID, and drivers is just the name.

Summary
\\abcd1 = physical ESXi host name.
Group Cpu(10:drivers) = a CPU resource group on that host, with ID 10 and name drivers.
% Used = the percentage of CPU utilization for that resource group/world.
No single VM is referred to directly in the path \\abcd1\Group Cpu(10:drivers)\% Used; rather, abcd1 is the ESXi server, which can (and typically does) manage many VMs or resource pools in esxtop.







You said:
OK...but drivers could be the name of a VM? That is a typical case? I'm trying to write a python script that needs to get a particular header name given a VM name and a metric. I always want it for the whole group. Is there a regex I can use to get that? The CSV data will have been loaded into a pandas dataframe
ChatGPT said:
Reasoned for 8 seconds
First, let’s clarify how VM names appear in esxtop CSV headers:

Yes, drivers could be the name of a VM (or a resource pool). It’s essentially the “label” ESXi uses for that “world” (ID = 10 in your example).
If your goal is to match a particular VM name (e.g., drivers) and a particular metric (e.g., % Used) for the whole group (i.e., the aggregated VM usage, not a specific vCPU), you’ll often see something like:
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\\MyEsxiHost\Group Cpu(10:drivers)\% Used
or possibly:
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\\MyEsxiHost\VM Cpu(10:drivers)\% Used
(depending on your version of esxtop and how the CSV was generated).
Matching by Regex in Pandas
If your Pandas DataFrame has columns named like this, you can use regex to filter for the column(s) you need. For example, suppose you have a variable:

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vm_name = "drivers"
metric_name = "% Used"
You can build a pattern that looks for a header of the form:

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\\<hostname>\Group Cpu(<number>:<vm_name>)\<metric_name>
1. Basic Regex Pattern
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import re

pattern = rf"^\\\\[^\\]+\\Group Cpu\(\d+:{vm_name}\)\\{metric_name}$"
Explanation of the pattern:

^ and $ ensure we match the entire string (not just a substring).
\\\\[^\\]+\\ matches a backslash-escaped hostname (like \\abcd1\):
\\\\ is Python’s double-escaped backslash for a single literal \\.
[^\\]+ is one or more non-backslash characters (the hostname).
Then another \\ is the literal backslash delimiter.
Group Cpu\( is the literal string "Group Cpu(".
\d+ means one or more digits for the “world ID.”
: followed by the vm_name (in your case, "drivers").
\)\\ closes the parenthesis and matches the next backslash.
Then metric_name (like "% Used"), which must be escaped if it has special characters:
In a raw string rf"...", % is fine, but spaces might need some caution. Typically it’s okay as-is in a raw string.
You can then apply this to your DataFrame columns:

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matching_cols = df.filter(regex=pattern).columns
print(matching_cols)
If you expect only one matching column, you might just do:

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matched_df = df.filter(regex=pattern)
# or if you want it as a Series:
my_series = df[matched_df.columns[0]]
2. Handling Variation in “Group” vs. “VM”
Sometimes the CSV might label the VM row as VM Cpu(...) instead of Group Cpu(...). If you want to be flexible and match both, you can alter the regex:

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pattern = rf"^\\\\[^\\]+\\(Group|VM) Cpu\(\d+:{vm_name}\)\\{metric_name}$"
This will match either "Group Cpu(...)" or "VM Cpu(...)".

3. Handling Additional Metrics
If you have multiple metrics (e.g., % Used, %RDY, %CSTP) and want any of them, you could do something like:

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pattern = rf"^\\\\[^\\]+\\Group Cpu\(\d+:{vm_name}\)\\% (Used|RDY|CSTP)$"
Or more flexibly:

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pattern = rf"^\\\\[^\\]+\\Group Cpu\(\d+:{vm_name}\)\\%\s+\S+$"
(where \s+\S+ will capture % plus some non-whitespace text for the metric name).

Putting It All Together
If you always know:

The VM name (e.g., drivers).
The metric name (e.g., % Used).
That it’s under Group Cpu(...).
You can simply do:

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import pandas as pd
import re

df = pd.read_csv("esxtop_export.csv")

vm_name = "drivers"
metric = "% Used"

pattern = rf"^\\\\[^\\]+\\Group Cpu\(\d+:{vm_name}\)\\{metric}$"

# Filter to columns matching that pattern:
matched_cols = df.filter(regex=pattern).columns
if len(matched_cols) == 0:
    print("No columns matched the pattern.")
else:
    print("Matched columns:", matched_cols)
    # For instance, pick the first match:
    usage_series = df[matched_cols[0]]
    print(usage_series.head())
This approach ensures you’re grabbing the aggregate “Group CPU” usage for the “drivers” VM (or resource pool), not per-vCPU counters.

Summary
drivers can indeed be a VM’s name or a resource pool label.

The \\abcd1\Group Cpu(10:drivers)\% Used format typically signifies “the % Used CPU metric for the resource (VM) named drivers with ID 10 on host abcd1.”

Regex is the simplest way to programmatically find the right column in a Pandas DataFrame of esxtop CSV data. A sample pattern might be:

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r'^\\\\[^\\]+\\Group Cpu\(\d+:drivers\)\\% Used$'






You said:
Is there anyway just to get the column name? It looks like this will give me a new dataframe that just has that column:
matched_df = df.filter(regex=pattern)
# or if you want it as a Series:
my_series = df[matched_df.columns[0]]
ChatGPT said:
Reasoned for a few seconds
Yes, absolutely. You can grab just the column name (or names) in a couple of ways:

1. Using the columns from the filtered DataFrame
When you run:

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matched_df = df.filter(regex=pattern)
matched_df is a new DataFrame containing only the columns that matched your regex. You can then get the matching column names via:

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matching_cols = matched_df.columns
If you just want the first matching column name, do:

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col_name = matched_df.columns[0]
(assuming you’re sure that at least one column matches!)

2. Filtering columns directly (list comprehension)
Alternatively, you can apply a regex match or search on df.columns directly:

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import re

matching_cols = [
    col for col in df.columns
    if re.match(pattern, col)
]
This way, matching_cols is a list of column names that match.

In both approaches, if you’re only expecting a single match for your pattern, just pick matching_cols[0]. Otherwise, you can keep the entire list if there might be more than one matching column.

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You said:
I'm working with pretty large files (700MB-2.5GB) and I have about 12 of them. I need to create plots for a bunch of metrics. I would prefer to load them all into memory and do this all at once, on a machine with 32GB of RAM. I looks like when they get loaded and I do the work they end up having a larger footprint in memory, so I'm worried I will start hitting swap. There are a couple of things I think I can do to filter these down though, but I want to make sure I'm doing it correctly with pandas to minimize the memory footprint for each one:
- filter to only the columns that I care about (using the regex we discussed and a list of metrics/vms)
- only look at the last 20 minutes based on a timestamp column
- downsample--the samples are currently at 2s and I want to average them at 10s
ChatGPT said:
Reasoned for 9 seconds
When working with very large CSV files (700MB–2.5GB each) and limited memory (32GB of RAM), you want to minimize your in-memory footprint as much as possible. Below are some strategies and sample code to:

Filter only desired columns (using regex or lists).
Filter only the last 20 minutes of data.
Downsample from 2s intervals to 10s intervals by averaging.
All of these will help reduce the data loaded and retained in memory at any point in time.

1. Identify and Load Only the Columns You Need
By default, pandas.read_csv will load all columns, which can be very large for esxtop CSVs that contain hundreds or thousands of counters. You can pre-filter columns with usecols=.... The challenge is that esxtop column names can be quite long and variable, so you often need to figure out the columns to keep before actually reading the full file.

Strategy A: Partial Read + Determine Columns
You can do a partial read (just the header row) to see all column names, pick out the ones you need, then re-read the CSV using usecols.

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import pandas as pd
import re

csv_file = "large_esxtop.csv"

# 1. First pass: read just the header (nrows=0).
df_header = pd.read_csv(csv_file, nrows=0)
all_columns = df_header.columns

# 2. Build a regex or a set of columns you want:
#    Let's say you want columns matching 'Group Cpu(\\d+:drivers)' and '% Used'
#    plus a timestamp column "Timestamp" (example name).
vm_name = "drivers"
metric = "% Used"

pattern = rf"^\\\\[^\\]+\\Group Cpu\(\d+:{vm_name}\)\\{metric}$"

# We'll keep the timestamp column plus whatever matches the pattern:
keep_cols = ["Timestamp"]  # or whatever your timestamp column is named
for col in all_columns:
    if re.match(pattern, col):
        keep_cols.append(col)

# 3. Now re-read the CSV, but only load those columns:
df = pd.read_csv(csv_file, usecols=keep_cols, parse_dates=["Timestamp"])
Notes:

parse_dates=["Timestamp"] will automatically convert the “Timestamp” column to a datetime64[ns]. Adjust the column name to whatever your actual time column is.
By using usecols=keep_cols, pandas will only load those columns into memory, drastically reducing usage.
2. Filter to the Last 20 Minutes
If you only need the last 20 minutes of data, you can immediately discard older rows after loading. This is easiest if your CSV is chronologically sorted by timestamp. Once the data is in a DataFrame:

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# Let's assume df has "Timestamp" in ascending order
# so the last 20 minutes are "Timestamp > max_T - 20 min"
# 1. Find the max timestamp in the file
max_t = df["Timestamp"].max()

# 2. Compute the cutoff
import datetime
cutoff = max_t - pd.Timedelta(minutes=20)

# 3. Filter the data
df = df[df["Timestamp"] >= cutoff]
This can significantly reduce your DataFrame size if the file contained days or hours of data.

Memory note: Even better, if the CSV is huge, you might consider a chunking approach (see below) to avoid reading the entire dataset just to keep the last 20 minutes. But if you’re sure you have enough memory to read and then trim, this method is simpler.

3. Downsample from 2s to 10s
Now that you have a DataFrame containing only the columns you care about and only the last 20 minutes, you can downsample to 10-second intervals. The most pandas-friendly way is:

Convert your timestamp column into a DateTimeIndex (if not already).
Use resample to average (or sum, etc.) every 10 seconds.
For example:

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# Ensure Timestamp is a DateTimeIndex
df = df.set_index("Timestamp")

# Now resample every 10s and mean
df_10s = df.resample("10S").mean()

# Reset index if you prefer a regular column
df_10s = df_10s.reset_index()
If your data is not strictly continuous, you will still get a row for every 10s boundary in that range. Missing intervals will have NaN. That’s typical for resampling, but keep in mind how to handle it. If you only want intervals that actually exist in your data, you could do something like:

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df_10s = df.resample("10S").mean().dropna(how='all')
4. Consider a Chunking Approach if Memory Still Spikes
If 700MB–2.5GB CSV × 12 files is still too large in memory, you may need a chunked read strategy:

Read the CSV in chunks (e.g., chunksize=200000 rows).
Filter each chunk’s columns (with usecols or by dropping columns after reading).
Convert timestamps and keep only rows that fall within the last 20 minutes.
Aggregate or resample within each chunk.
Concatenate or further aggregate the partial results to get a final result.
A rough outline:

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import pandas as pd

csv_file = "large_esxtop.csv"
chunk_iter = pd.read_csv(csv_file, 
                         usecols=keep_cols, 
                         parse_dates=["Timestamp"],
                         chunksize=200_000)

resampled_chunks = []
for chunk in chunk_iter:
    # Filter rows in chunk to last 20 min
    cutoff = desired_max_t - pd.Timedelta(minutes=20)
    chunk = chunk[chunk["Timestamp"] >= cutoff]
    
    # If chunk is empty after filtering, skip
    if chunk.empty:
        continue
    
    # Resample at 10s
    chunk = chunk.set_index("Timestamp").resample("10S").mean()
    resampled_chunks.append(chunk)

# Combine all resampled chunks
if resampled_chunks:
    df_10s = pd.concat(resampled_chunks)
    # If needed, resample again in case boundaries overlapped
    df_10s = df_10s.resample("10S").mean()
    df_10s = df_10s.reset_index()
else:
    df_10s = pd.DataFrame()  # or handle "no data" case
This way, you never load the entire file at once; you process it piece by piece. If you really only care about the last 20 minutes, you can also figure out the earliest needed timestamp and skip reading older chunks altogether. However, that typically means you need to know the maximum timestamp in advance (which can be done with a quick pass at just the “Timestamp” column).

5. Summary of Memory-Saving Tips
Use usecols to restrict columns so you don’t load every single esxtop metric.
Convert the timestamp to datetime on read (via parse_dates) so you can quickly filter by time.
Filter out unneeded rows immediately after loading (e.g., keep only last 20 minutes).
Downsample as soon as possible to reduce row count.
If the data is still too large, switch to a chunk-based approach.
With 32GB of RAM, you might manage to load 2–3GB CSV files in memory if they expand to, say, 10–12GB each. But if you have 12 files, you may well exceed your memory limit if you load them all simultaneously. Hence, a chunk or iterative approach is often more robust for large data sets.

Good Luck!
By selectively loading only the columns you truly need, immediately filtering rows by time, and downsampling early, you can greatly reduce how much data ends up in memory at once. This approach should let you plot your metrics without hitting swap or running out of RAM.