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author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2022-06-09 04:52:47 +0000 |
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committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2022-06-09 04:52:57 +0000 |
commit | 00151562145df50cc65e9902d52d5fa77f89fe50 (patch) | |
tree | 2737716802f6725a5074d606ec8fe5422c58a83c /libnetdata/onewayalloc/README.md | |
parent | Releasing debian version 1.34.1-1. (diff) | |
download | netdata-00151562145df50cc65e9902d52d5fa77f89fe50.tar.xz netdata-00151562145df50cc65e9902d52d5fa77f89fe50.zip |
Merging upstream version 1.35.0.
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'libnetdata/onewayalloc/README.md')
-rw-r--r-- | libnetdata/onewayalloc/README.md | 71 |
1 files changed, 71 insertions, 0 deletions
diff --git a/libnetdata/onewayalloc/README.md b/libnetdata/onewayalloc/README.md new file mode 100644 index 00000000..1f459c26 --- /dev/null +++ b/libnetdata/onewayalloc/README.md @@ -0,0 +1,71 @@ +<!-- +title: "One Way Allocator" +custom_edit_url: https://github.com/netdata/netdata/edit/master/libnetdata/onewayallocator/README.md +--> + +# One Way Allocator + +This is a very fast single-threaded-only memory allocator, that minimized system calls +when a lot of memory allocations needs to be made to perform a task, which all of them +can be freed together when the task finishes. + +It has been designed to be used for netdata context queries. + +For netdata to perform a context query, it builds a virtual chart, a chart that contains +all the dimensions of the charts having the same context. This process requires allocating +several structures for each of the dimensions to attach them to the virtual chart. All +these data can be freed immediately after the query finishes. + +## How it works + +1. The caller calls `ONEWAYALLOC *owa = onewayalloc_create(sizehint)` to create an OWA. + Internally this allocates the first memory buffer with size >= `sizehint`. + If `sizehint` is zero, it will allocate 1 hardware page (usually 4kb). + No need to check for success or failure. As with `mallocz()` in netdata, a `fatal()` + will be called if the allocation fails - although this will never fail, since Linux + does not really check if there is memory available for `mmap()` calls. + +2. The caller can then perform any number of the following calls to acquire memory: + - `onewayalloc_mallocz(owa, size)`, similar to `mallocz()` + - `onewayalloc_callocz(owa, nmemb, size)`, similar to `callocz()` + - `onewayalloc_strdupz(owa, string)`, similar to `strdupz()` + - `onewayalloc_memdupz(owa, ptr, size)`, similar to `mallocz()` and then `memcpy()` + +3. Once the caller has done all the work with the allocated buffers, all memory allocated + can be freed with `onewayalloc_destroy(owa)`. + +## How faster it is? + +On modern hardware, for any single query the performance improvement is marginal and not +noticeable at all. + +We performed the following tests using the same huge context query (1000 charts, +100 dimensions each = 100k dimensions) + +1. using `mallocz()`, 1 caller, 256 queries (sequential) +2. using `mallocz()`, 256 callers, 1 query each (parallel) +3. using `OWA`, 1 caller, 256 queries (sequential) +4. using `OWA`, 256 callers, 1 query each (parallel) + +Netdata was configured to use 24 web threads on the 24 core server we used. + +The results are as follows: + +### sequential test + +branch|transactions|time to complete|transaction rate|average response time|min response time|max response time +:---:|:---:|:---:|:---:|:---:|:---:|:---:| +`malloc()`|256|322.35s|0.79/sec|1.26s|1.01s|1.87s +`OWA`|256|310.19s|0.83/sec|1.21s|1.04s|1.63s + +For a single query, the improvement is just marginal and not noticeable at all. + +### parallel test + +branch|transactions|time to complete|transaction rate|average response time|min response time|max response time +:---:|:---:|:---:|:---:|:---:|:---:|:---:| +`malloc()`|256|84.72s|3.02/sec|68.43s|50.20s|84.71s +`OWA`|256|39.35s|6.51/sec|34.48s|20.55s|39.34s + +For parallel workload, like the one executed by netdata.cloud, `OWA` provides a 54% overall speed improvement (more than double the overall +user-experienced speed, including the data query itself). |