Steps to reduce ArangoDB’s resource footprint
This is an update of the 2016 blog post How to put ArangoDB to Spartan-Mode.
Therefore let’s focus on our new storage engine, and make a configuration to use RocksDB the Spartan way.
Reducing the Memory Footprint
Let us assume our test system is a big server with many cores and a lot of memory. However, we intend to run other services on this machine as well. Therefore we want to restrict the memory usage of ArangoDB. By default, ArangoDB in version 3.4 tries to use as much memory as possible. Using memory accesses instead of disk accesses is faster and in the database business performance rules. ArangoDB comes with a default configuration with that in mind. But sometimes being a little less grabby on system resources may still be fast enough, for example if your working data set isn’t huge. The goal is to reduce the overall memory footprint.
There are the following big areas, which might eat up memory:
- WAL (Write Ahead Log)
- Write Buffers
RocksDB writes into memory buffers mapped to on-disk blocks first. At some point, the memory buffers will be full and have to be written to disk. In order to support high write loads, RocksDB might open a lot of these memory buffers.
Under normal write load, the write buffers will use less than 1 GByte of memory. If you are tight on memory, or your usage pattern doesn’t require this, you can reduce these settings:
--rocksdb.max-total-wal-size 1024000 --rocksdb.write-buffer-size 2048000 --rocksdb.max-write-buffer-number 2 --rocksdb.total-write-buffer-size 81920000 --rocksdb.dynamic-level-bytes false
Above settings will
- restrict the number of outstanding memory buffers
- limit the memory usage to around 100 MByte
During import or updates, the memory consumption may still grow bigger. On the other hand, these restrictions will have an impact on the maximal write performance. You should not go below above numbers.
--rocksdb.block-cache-size 2560000 --rocksdb.enforce-block-cache-size-limit true
These settings are the counterpart of the settings from the previous section. As soon as the memory buffers have been persisted to disk, answering read queries implies to read them back into memory. The above option will limit the number of cached buffers to a few megabytes. If possible, this setting should be configured as large as the hot-set size of your dataset.
These restrictions may have an impact on query performance.
This option limits the ArangoDB edge cache to 10 MB. If you don’t have a graph use-case and don’t use edge collections, it is possible to use the minimum without a performance impact. In general, this should correspond to the size of the hot-set.
In addition to all buffers, a query will use additional memory during its execution, to process your data and build up your result set. This memory is used during the query execution only and will be released afterwards, in contrast to the held memory for buffers.
Query Memory Usage
By default, queries will build up their full results in memory. While you can fetch the results batch by batch by using a cursor, every query needs to compute the entire result first before you can retrieve the first batch. The server also needs to hold the results in memory until the corresponding cursor is fully consumed or times out. Building up the full results reduces the time the server has to work with collections at the cost of main memory.
In ArangoDB version 3.4 we introduced streaming cursors with somewhat inverted properties: less peak memory usage, longer access to the collections. Streaming is possible on document level, which means that it can not be applied to all query parts. For example, a `MERGE()` of all results of a subquery can’t be streamed (the result of the operation has to be built up fully). Nonetheless, the surrounding query may be eligible for streaming.
Aside from streaming cursors, ArangoDB offers the possibility to specify a memory limit which a query shouldn’t exceed. If it does, the query will be aborted. Memory statistics are checked between execution blocks, which correspond to lines in the `explain` output. That means queries which require functions may require more memory for intermediate processing, but this won’t kill the query because the memory
You can use `LIMIT` operations in AQL queries to reduce the number of documents that need to be inspected and processed. This is not always what happens under the hood however. Other operations may lead to an intermediate result being computed before any limit is applied. Recently, we added a new ability to the optimizer: the Sort-Limit Optimization in AQL. In short, a `SORT` combined with a `LIMIT` operation only keeps as many documents in memory during sorting as the subsequent `LIMIT` requires. This improvement will ship with ArangoDB v3.5.0.
The server collects statistics regularly, which it shows you in the web interface. You will have a light query load even if your application is idle because of the statistics. You can disable them if desired:
- Backend parts of the web interface
- Foxx Apps
- Foxx Queues
- User-defined AQL functions
V8 for the Desperate
You should not use the following settings unless there are very good reasons, like a local development system on which performance is not critical or an embedded system with very limited hardware resources!
If you are very tight on memory, and you are sure that you do not need V8, you can disable it completely:
In consequence, the following features will not be available:
- Backend parts of the web interface
- Foxx Apps
- Foxx Queues
- User-defined AQL functions
We can’t really reduce CPU usage, but the number of threads running in parallel. Again, you should not do this unless there are very good reasons, like an embedded system. Note that this will limit the performance for concurrent requests, which may be okay for a local development system with you as only user.
The number of background threads can be limited in the following way:
--arangosearch.threads-limit 1 --rocksdb.max-background-jobs 4 --server.maintenance-threads 2 --server.maximal-threads 4 --server.minimal-threads 1
In general, the number of threads is selected to fit the machine. However, each thread requires at least 8 MB of stack memory. By sacrificing some performance for parallel execution it is possible to reduce this.
This option will make logging synchronous:
This is not recommended unless you only log errors and warnings.
In general, you should adjust the read buffers and edge cache on a standard server. If you have a graph use-case, you should go for a larger edge cache. For example, split the memory 50:50 between read buffers and edge cache. If you have no edges then go for a minimal edge cache and use most of the memory for the read buffers.
For example, if you have a machine with 40 GByte of memory and you want to restrict ArangoDB to 20 GB of that, use 10 GB for the edge cache and 10 GB for the read buffers if you use graph features.
Please keep in mind that during query execution additional memory will be used for query results temporarily. If you are tight on memory, you may want to go for 7 GB each instead.
If you have an embedded system or your development laptop, you can use all of the above settings to lower the memory footprint further. For normal operation, especially production, these settings are not recommended.
Linux System Configuration
The main deployment target for ArangoDB is Linux. As you’ve learned above ArangoDB and its innards work a lot with memory. Thus its vital to know how ArangoDB and the Linux kernel interact on that matter. The linux kernel offers several modes of how it will manage memory. You can influence this via the proc filesystem, the file `/etc/sysctl.conf` or a file in `/etc/sysctl.conf.d/` which your system will apply to the kernel settings at boot time. The settings as named below are intended for the sysctl infrastructure, meaning that they map to the `proc` filesystem as `/proc/sys/vm/overcommit_memory`.
After studying the kernel documentation our former recommendation was to set `vm.overcommit_memory` to `2` – which we (and others) found out is not good in all situations. It seems that there is an issue with this setting in combination with the bundled memory allocator ArangoDB ships with (jemalloc) in some environments.
The allocator demands consecutive blocks of memory from the kernel, which are also mapped to on-disk blocks. This is done on behalf of the server process (arangod). The process may use some chunks of a block for a long time span, but others only for a short while and therefore release the memory. It is then up to the allocator to return the freed parts back to the kernel. Because it can only give back consecutive blocks of memory, it has to split the large block into multiple small blocks and can then return the unused ones.
With an `vm.overcommit_memory` kernel settings value of `2`, the allocator may have trouble with splitting existing memory mappings, which makes the number of memory mappings of an arangod server process grow over time. This can lead to the kernel refusing to hand out more memory to the arangod process, even if more physical memory is available. The kernel will only grant up to `vm.max_map_count` memory mappings to each process, which defaults to 65530 on many Linux environments.
Another issue when running jemalloc with `vm.overcommit_memory` set to `2` is that for some workloads the amount of memory that the Linux kernel tracks as “committed memory” also grows over time and never decreases. Eventually, arangod may not get any more memory simply because it reaches the configured overcommit limit (physical RAM * `overcommit_ratio` + swap space).
The solution is to modify the value of `vm.overcommit_memory` from `2` to either `0` or `1`. This will fix both of these problems. We still observe ever-increasing virtual memory consumption when using jemalloc regardless of the overcommit setting, but in practice this should not cause any issues. Adjusting the value to either `0` or `1` should improve the situation. `0` is the Linux kernel default and also the setting we recommend.
For the sake of completeness, let us also mention another way to address the problem: use a different memory allocator. This requires to compile ArangoDB from the source code without jemalloc (`-DUSE_JEMALLOC=Off` in the call to cmake). With the system’s libc allocator you should see quite stable memory usage. We also tried another allocator, precisely the one from `libmusl`, and this also shows quite stable memory usage over time. What holds us back to change the bundled allocator are that it is a non-trivial change and because jemalloc has very nice performance characteristics for massively multi-threaded processes otherwise.
Testing the Effects of Reduced I/O Buffers
- 15:50 – Start bigger import
- 16:00 – Start writing documents of ~60 KB size one at a time
- 16:45 – Add similar second writer
- 16:55 – Restart ArangoDB with the RocksDB write buffer configuration suggested above
- 17:20 – Buffers are full, write performance drops
- 17:38 – WAL rotation
What you see in above performance graph are the consequences of restricting the write buffers. Until we reach a 90% fill rate of the write buffers the server can almost follow the load pattern for a while at the cost of constantly increasing buffers. Once RocksDB reaches 90% buffer fill rate, it will significantly throttle the load to ~50%. This is expected according to the upstream documentation:
[…] a flush will be triggered […] if total mutable memtable size exceeds 90% of the limit. If the actual memory is over the limit, more aggressive flush may also be triggered even if total mutable memtable size is below 90%.
Since we only measured the disk I/O bytes, we don’t see that the document save operations also doubled in request time.
We showed how ArangoDB’s memory usage can be restricted and the CPU utilization be reduced by different configuration options:
- storage engine (RocksDB)
- edge cache
- server statistics
- background threads
- operating system / memory allocator (Linux)
There are settings to make it run on systems with very limited resources, but they may also be interesting for your development machine if you want to make it less taxing for the hardware and don’t work with much data. For production environments, we recommend to use less restrictive settings, to benchmark your setup and fine-tune the settings for maximal performance.
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