I have these 2 particular use cases:
Streaming jobs, writing 30mb every 5 seconds
Batch jobs, writing 500 gb every morning
The TTL of my tables in 1,5 years.
These writes can contain many updates, so, according to this table right here:
I should use the SizeTieredCompactionStrategy. However, how do I choose the correct parameters for it?
It has several parameters:
bucket_high
bucket_low
min_sstable_size
min_threshold
max_threshold
As a general proposition, it is very rare for operators to have to configure the size-tiered compaction sub-properties.
Unless you're very experienced with Cassandra, there just isn't any reason to reconfigure the defaults for STCS. That is why it is default compaction strategy out-of-the-box and is suitable for majority of workloads.
The exceptions are using TWCS for true time-series use cases and LCS for very read-heavy with hardly any writes. Cheers!
What is the difference between Scylla read path and Cassandra read path? When I stress Cassandra and Scylla then Scylla read performance poor by 5 times than Cassandra using 16 core and normal HDD.
I expect better read performance on Scylla compared to Cassandra using normal HDD, because my company doesn't provide SSD's.
Can someone please confirm, is it possible to achieve better read performance using normal HDD or not?
If yes, what changes required scylla config?. Please guide me!
Some other responses focused on write performance, but this isn't what you asked about - you asked about reads.
Uncached read performance on HDDs is bound to be poor in both Cassandra and Scylla, because reads from disk each requires several seeks on the HDD, and even the best HDD cannot do more than, say, 200 of those seeks per second. Even with a RAID of several of these disks, you will rarely be able to do more than, say, 1000 requests per second. Since a modern multi-core can do orders of magnitude more CPU work than 1000 requests per second, in both Scylla and Cassandra cases, you'll likely see free CPU. So Scylla's main benefit, of using much less CPU per request, will not even matter when the disk is the performance bottleneck. In such cases I would expect Scylla's and Cassandra's performance (I am assuming that you're measuring throughput when you talk about performance?) should be roughly the same.
If, still, you're seeing better throughput from Cassandra than Scylla, there are several details that may explain why, beyond the general client mis-configuration issues raised in other responses:
If you have low amounts of data, that can fit in memory, Cassandra's caching policy is better for your workload. Cassandra uses the OS's page cache, which reads whole disk pages and may cache multiple items in one read, as well as multiple index entries. While Scylla works differently, and has a row cache - only caching the specific data read. Scylla's caching is better for large volumes of data that do not fit in memory, but much worse when the data can fit in memory, until the entire data set has been cached (after everything is cached, it becomes very efficient again).
On HDDs, the details of compaction are very important for read performance - if in one setup you have more sstables to read, it can increase the number of reads and lower the performance. This can change depending on your compaction configuration, or even randomly (depending on when compaction was run last). You can check if this explains your performance issues by doing a major compaction ("nodetool compact") on both systems and checking the read performance afterwards. You can switch the compaction strategy to LCS to ensure that random-access read performance is better, at the cost of more write work (on HDDs, this can be a worthwhile compromise).
If you are measuring scan performance (reading an entire table) instead of reading individual rows, other issues become relevant: As you may have heard, Scylla subdivides each nodes into shards (each shard is a single CPU). This is fantastic for CPU-bounded work, but could be worse for scanning tables which aren't huge, because each sstable is now smaller and the amount of contiguous data you can read before needing to seek again is lower.
I don't know which of these differences - or something else - is causing performance of your use-case to be lower in Scylla, but I please keep in mind that whatever you fix, your performance is always going to be bad with HDDs. With SDDs, we've measured in the past more than a million random-access read requests per second on a single node. HDDs cannot come anything close. If you really need optimum performance or performance per dollar, SDDs are really the way to go.
There can be various reasons why you are not getting the most out of your Scylla Cluster.
Number of concurrent connections from your clients/loaders is not high enough, or you're not using sufficient amount of loaders. In such case, some shards will be doing all the work, while others will be mostly idle. You want to keep your parallelism high.
Scylla likes have a minimum of 2 connections per shard (you can see the number of shards in /etc/scylla.d/cpuset.conf)
What's the size of your dataset? Are you reading a large amount of partitions or just a few? You might be hitting a hot partition situation
I strongly recommend reading the following docs that will provide you more insights:
https://www.scylladb.com/2019/03/27/best-practices-for-scylla-applications/
https://docs.scylladb.com/operating-scylla/benchmarking-scylla/
#Sateesh, I want to add to the answer by #TomerSan that both Cassandra and ScyllaDB utilize the same disk storage architecture (LSM). That means that they have relatively the same disk access patterns because the algorithms are largely the same. The LSM trees were built with the idea in mind that it is not necessary to do instant in-place updates. It consists of immutable data buckets that are large continuous pieces of data on disk. That means less random IO, more sequential IO for which the HDD works great (not counting utilized parallelism by modern database implementations).
All the above means that the difference that you see, is not induced by the difference in how those databases use a disk. It must be related to the configuration differences and what happens underneath. Maybe ScyllaDB tries to utilize more parallelism or more aggressively do compaction. It depends.
In order to be able to say anything specific, please share your tests, envs, and configurations.
Both databases use LSM tree but Scylla has thread-per-core architecture on top plus we use O_Direct while C* uses the page cache. Scylla also has a sophisticated IO scheduler that makes sure not to overload the disk and thus scylla_setup runs a benchmark automatically to tune. Check your output of it in io.conf.
There are far more things to review, better to send your data to the mailing list. In general, Scylla should perform better in this case as well but your disk is likely to be the bottleneck in both cases.
As a summary I would say Scylladb and cassandra have the same read / write path
memtable, commitlog, sstable.
However implementation is very different:
- cassandra rely on OS for low level IO and network (most DBMS does)
- scylladb rely on its own lib (seastar) to handle IO and network at a low level independently from OS page cache etc. This is why they can provide feature such as workload scheduling within the same cluster that would be very hard to implement in cassandra.
I have seen this warning everywhere but cannot find any detailed explanation on this topic.
For starters
The maximum number of cells (rows x columns) in a single partition is
2 billion.
If you allow a partition to grow unbounded you will eventually hit this limitation.
Outside that theoretical limit, there are practical limitations tied to the impacts large partitions have on the JVM and read times. These practical limitations are constantly increasing from version to version. This practical limitation is not fixed but variable with data model, query patterns, heap size, and configurations which makes it hard to be give a straight answer on whats too large.
As of 2.1 and early 3.0 releases, the primary cost on reads and compactions comes from deserializing the index which marks a row every column_index_size_in_kb. You can increase the key_cache_size_in_mb for reads to prevent unnecessary deserialization but that reduces heap space and fills old gen. You can increase the column index size but it will increase worst case IO costs on reads. Theres also many different settings for CMS and G1 to tune the impact of a huge spike in object allocations when reading these big partitions. There are active efforts on improving this so in the future it might no longer be the bottleneck.
Repairs also only go down to (in best case scenario) the partition level. So if say you are constantly appending to a partition, and a hash of that partition on 2 nodes are compared at not an exact time (distributed system essentially guarantees this), the entire partition must be streamed over to ensure consistency. Incremental repairs can reduce impact of this, but your still streaming massive amounts of data and fluctuating disk significantly which will then need to be compacted together unnecessarily.
You can probably keep adding onto this of corner cases and scenarios that have issues. Many times large partitions are possible to read, but the tuning and corner cases involved in them are not really worth it, better to just design data model to be friendly with how Cassandra expects it. I would recommend targeting 100mb but you can go far beyond that comfortably. Into the Gbs and you will need to start consider tuning for it (depending on data model, use case etc).
I have 6 Cassandra nodes mainly used for writing (95%).
What's the best approach to inserting data - individual inserts or batches? reason says batches are to be used, while keeping the "batch size" under 5kb to avoid node instability:
https://issues.apache.org/jira/browse/CASSANDRA-6487
Do these 5kb concern the size of the queries, as in number of chars * bytes_per_char? are there any performance drawbacks to fully running individual inserts?
Batch will increase performance if used for a single partition. You are able to get more through put of data inserted.
I am reading cassandra yml to tune some parameters to optimize the performance.
# Throttles compaction to the given total throughput across the entire
# system. The faster you insert data, the faster you need to compact in
# order to keep the sstable count down, but in general, setting this to
# 16 to 32 times the rate you are inserting data is more than sufficient.
# Setting this to 0 disables throttling. Note that this account for all types
# of compaction, including validation compaction.
compaction_throughput_mb_per_sec: 16
Here cassandra have recommended to keep "compaction_throughput_mb_per_sec" 16 to 32 times of rate of data insert.We are inserting data with bulk loader which insert data at very high rate. Should I change this setting? OR it should be kept as it is ?
So do you have an idea on how much data your bulk loader will push in per second? Is that a sustained load or is it bursty? If you do know that, then you basically just follow the guideline in the config files comment for that parameter.
I suspect that you don't have that information, in which case the question is whether your system is misbehaving or not (do you currently see issues?). Sorry I don't know of a good way to otherwise predict that, this is a tuning exercise. You want to find a value that guarantees that Cassandra is not falling behind on compaction, but at the same time you don't want compaction to kill you disk i/o. The only way to figure that out (that I know off) is to try and observe.