How To Analyze ScyllaDB Cluster Capacity

8 January 2025, 1:20 pm by ScyllaDB

Monitoring tips that can help reduce cluster size 2-5X without compromising latency Editor’s note: The following is a guest post by Andrei Manakov, Senior Staff Software Engineer at ShareChat. It was originally published on Andrei’s blog. I had the privilege of giving a talk at ScyllaDB Summit 2024, where I briefly addressed the challenge of analyzing the remaining capacity in ScyllaDB clusters. A good understanding of ScyllaDB internals is required to plan your computation cost increase when your product grows or to reduce cost if the cluster turns out to be heavily over-provisioned. In my experience, clusters can be reduced by 2-5x without latency degradation after such an analysis. In this post, I provide more detail on how to properly analyze CPU and disk resources. How Does ScyllaDB Use CPU? ScyllaDB is a distributed database, and one cluster typically contains multiple nodes. Each node can contain multiple shards, and each shard is assigned to a single core. The database is built on the Seastar framework and uses a shared-nothing approach. All data is usually replicated in several copies, depending on the replication factor, and each copy is assigned to a specific shard. As a result, every shard can be analyzed as an independent unit and every shard efficiently utilizes all available CPU resources without any overhead from contention or context switching. Each shard has different tasks, which we can divide into two categories: client request processing and maintenance tasks. All tasks are executed by a scheduler in one thread pinned to a core, giving each one its own CPU budget limit. Such clear task separation allows isolation and prioritization of latency-critical tasks for request processing. As a result of this design, the cluster handles load spikes more efficiently and provides gradual latency degradation under heavy load. [More details about this architecture].

Another interesting result of this design is that ScyllaDB supports workload prioritization. In my experience, this approach ensures that critical latency is not impacted during less critical load spikes. I can’t recall any similar feature in other databases. Such problems are usually tackled by having 2 clusters for different workloads. But keep in mind that this feature is available only in ScyllaDB Enterprise.

However, background tasks may occupy all remaining resources, and overall CPU utilization in the cluster appears spiky. So, it’s not obvious how to find the real cluster capacity. It’s easy to see 100% CPU usage with no performance impact. If we increase the critical load, it will consume the resources (CPU, I/O) from background tasks. Background tasks’ duration can increase slightly, but it’s totally manageable. The Best CPU Utilization Metric How can we understand the remaining cluster capacity when CPU usage spikes up to 100% throughout the day, yet the system remains stable? We need to exclude maintenance tasks and remove all these spikes from the consideration. Since ScyllaDB distributes all the data by shards and every shard has its own core, we take into account the max CPU utilization by a shard excluding maintenance tasks (you can find other task types here). In my experience, you can keep the utilization up to 60-70% without visible degradation in tail latency. Example of a Prometheus query: max(sum(rate(scylla_scheduler_runtime_ms{group!="compaction|streaming"})) by (instance, shard))/10

You can find more details about the ScyllaDB monitoring stack here. In this article, PromQL queries are used to demonstrate how to analyse key metrics effectively.

However, I don’t recommend rapidly downscaling the cluster to the desired size just after looking at max CPU utilization excluding the maintenance tasks. First, you need to look at average CPU utilization excluding maintenance tasks across all shards. In an ideal world, it should be close to max value. In case of significant skew, it definitely makes sense to find the root cause. It can be an inefficient schema with an incorrect partition key or an incorrect token-aware/rack-aware configuration in the driver. Second, you need to take a look at the average CPU utilization of excluded tasks for some your workload specific things. It’s rarely more than 5-10% but you might need to have more buffer if it uses more CPU. Otherwise, compaction will be too tight in resources and reads start to become more expensive with respect to CPU and disk. Third, it’s important to downscale your cluster gradually. ScyllaDB has an in-memory row cache which is crucial for ScyllaDB. It allocates all remaining memory for the cache and with the memory reduction, the hit rate might drop more than you expected. Hence, CPU utilization can be increased unilinearly and low cache hit rate can harm your tail latency. 1- (sum(rate(scylla_cache_reads_with_misses{})) / sum(rate(scylla_cache_reads{})))

I haven’t mentioned RAM in this article as there are not many actionable points. However, since memory cache is crucial for efficient reading in ScyllaDB, I recommend always using memory-optimized virtual machines. The more memory, the better.

Disk Resources ScyllaDB is a LSMT-based database. That means it is optimized for writing by design and any mutation will lead to new appending new data to the disk. The database periodically rewrites the data to ensure acceptable read performance. Disk performance plays a crucial role in overall database performance. You can find more details about the write path and compaction in the scylla documentation. There are 3 important disk resources we will discuss here: Throughput, IOPs and free disk space. All these resources depend on the disk type we attached to our ScyllaDB nodes and their quantity. But how can we understand the limit of the IOPs/throughput? There 2 possible options: Any cloud provider or manufacturer usually provides performance of their disks ; you can find it on their website. For example, NVMe disks from Google Cloud. The actual disk performance can be different compared to the numbers that manufacturers share. The best option might be just to measure it. And we can easily get the result. ScyllaDB performs a benchmark during installation to a node and stores the result in the file io_properties.yaml. The database uses these limits internally for achieving optimal performance. disks: - mountpoint: /var/lib/scylla/data read_iops: 2400000 //iops read_bandwidth: 5921532416//throughput write_iops: 1200000 //iops write_bandwidth: 4663037952//throughput file: io_properties.yaml Disk Throughput sum(rate(node_disk_read_bytes_total{})) / (read_bandwidth * nodeNumber) sum(rate(node_disk_written_bytes_total{})) / (write_bandwidth * nodeNumber) In my experience, I haven’t seen any harm with utilization up to 80-90%. Disk IOPs sum(rate(node_disk_reads_completed_total{})) / (read_iops * nodeNumber) sum(rate(node_disk_writes_completed_total{})) / (write_iops * nodeNumber) Disk free space It’s crucial to have significant buffer in every node. In case you’re running out of space, the node will be basically unavailable and it will be hard to restore it. However, additional space is required for many operations: Every update, write, or delete will be written to the disk and allocate new space. Compaction requires some buffer during cleaning the space. Back up procedure. The best way to control disk usage is to use Time To Live in the tables if it matches your use case. In this case, irrelevant data will expire and be cleaned during compaction. I usually try to keep at least 50-60% of free space. min(sum(node_filesystem_avail_bytes{mountpoint="/var/lib/scylla"}) by (instance)/sum(node_filesystem_size_bytes{mountpoint="/var/lib/scylla"}) by (instance)) Tablets Most apps have significant load variations throughout the day or week. ScyllaDB is not elastic and you need to have provisioned the cluster for the peak load. So, you could waste a lot of resources during night or weekends. But that could change soon. A ScyllaDB cluster distributes data across its nodes and the smallest unit of the data is a partition uniquely identified by a partition key. A partitioner hash function computes tokens to understand in which nodes data are stored. Every node has its own token range, and all nodes make a ring. Previously, adding a new node wasn’t a fast procedure because it required copying (it is called streaming) data to a new node, adjusting token range for neighbors, etc. In addition, it’s a manual procedure. However, ScyllaDB introduced tablets in 6.0 version, and it provides new opportunities. A Tablet is a range of tokens in a table and it includes partitions which can be replicated independently. It makes the overall process much smoother and it increases elasticity significantly. Adding new nodes takes minutes and a new node starts processing requests even before full data synchronization. It looks like a significant step toward full elasticity which can drastically reduce server cost for ScyllaDB even more. You can read more about tablets here. I am looking forward to testing tablets closely soon. Conclusion Tablets look like a solid foundation for future pure elasticity, but for now, we’re planning clusters for peak load. To effectively analyze ScyllaDB cluster capacity, focus on these key recommendations: Target max CPU utilization (excluding maintenance tasks) per shard at 60–70%. Ensure sufficient free disk space to handle compaction and backups. Gradually downsize clusters to avoid sudden cache degradation.  

ScyllaDB University and Training Updates

2 January 2025, 1:09 pm by ScyllaDB

It’s been a while since my last update. We’ve been busy improving the existing ScyllaDB training material and adding new lessons and labs. In this post, I’ll survey the latest developments and update you on the live training event taking place later this month. You can discuss these topics (and more!) on the community forum. Say hello here. ScyllaDB University LIVE Training In addition to the self-paced online courses you can take on ScyllaDB University (see below), we host online live training events. These events are a great opportunity to improve your NoSQL and ScyllaDB skills, get hands-on practice, and get your questions answered by our team of experts. The next event is ScyllaDB University LIVE, which will occur 29th of January 29. As usual, we’re planning on having two tracks, an Essentials, and an Advanced track. However, this time we’ll change the format and make each track a complete learning path. Stay tuned for more details, and I hope to see you there. Save your spot at ScyllaDB University LIVE ScyllaDB University Content Updates ScyllaDB University is our online learning platform where you can learn about NoSQL and about ScyllaDB and get some hands-on experience. It includes many different self-paced lessons, meaning you can study whenever you have some free time and continue where you left off. The material is free and all you have to do is create a user account. We recently added new lessons and updated many existing ones. All of the following topics were added to the course S201: Data Modeling and Application Development. Start learning New in the How To Write Better Apps Lesson General Data Modeling Guidelines This lesson discusses key principles of NoSQL data modeling, emphasizing a query-driven design approach to ensuring efficient data distribution and balanced workloads. It highlights the importance of selecting high-cardinality primary keys, avoiding bad access patterns, and using ScyllaDB Monitoring to identify and resolve issues such as Hot Partitions and Large Partitions. Neglecting these practices can lead to slow performance, bottlenecks, and potentially unreadable data – underscoring the need for using best practices when creating your data model. To learn more, you can explore the complete lesson here. Large Partitions and Collections This lesson provides insights into common pitfalls in NoSQL data modeling, focusing on issues like large partitions, collections, and improper use of ScyllaDB features. It emphasizes avoiding large partitions due to the impact on performance and demonstrates this with real-world examples and Monitoring data. Collections should generally remain small to prevent high latency. The schema used depends on the use case and on the performance requirements. Practical advice and tools are offered for testing and monitoring. You can learn more in the complete lesson here. Hot Partitions, Cardinality and Tombstones This lesson explores common challenges in NoSQL databases, focusing on hot partitions, low cardinality keys, and tombstones. Hot partitions cause uneven load and bottlenecks, often due to misconfigurations or retry storms. Having many tombstones can degrade read performance due to read amplification. Best practices include avoiding retry storms, using efficient full-table scans over low cardinality views and preferring partition-level deletes to minimize tombstone buildup. Monitoring tools and thoughtful schema design are emphasized for efficient database performance. You can find the complete lesson here. Diagnosis and Prevention This lesson covers strategies to diagnose and prevent common database issues in ScyllaDB, such as large partitions, hot partitions, and tombstone-related inefficiencies. Tools like the nodetool toppartitions command help identify hot partition problems, while features like per-partition rate limits and shard concurrency limits manage load and prevent contention. Properly configuring timeout settings avoids retry storms that exacerbate hot partition problems. For tombstones, using efficient delete patterns helps maintain performance and prevent timeouts during reads. Proactive monitoring and adjustments are emphasized throughout. You can see the complete lesson here. New in the Basic Data Modeling Lesson CQL and the CQL Shell The lesson introduces the Cassandra Query Language (CQL), its similarities to SQL, and its use in ScyllaDB for data definition and manipulation commands. It highlights the interactive CQL shell (CQLSH) for testing and interaction, alongside a high level overview of drivers. Common data types and collections like Sets, Lists, Maps, and User-Defined Types in ScyllaDB are briefly mentioned. The “Pet Care IoT” lab example is presented, where sensors on pet collars record data like heart rate or temperature at intervals. This demonstrates how CQL is applied in database operations for IoT use cases. This example is used in labs later on. You can watch the video and complete lesson here. Data Modeling Overview and Basic Concepts The new video introduces the basics of data modeling in ScyllaDB, contrasting NoSQL and relational approaches. It emphasizes starting with application requirements, including queries, performance, and consistency, to design models. Key concepts such as clusters, nodes, keyspaces, tables, and replication factors are explained, highlighting their role in distributed data systems. Examples illustrate how tables and primary keys (partition keys) determine data distribution across nodes using consistent hashing. The lesson demonstrates creating keyspaces and tables, showing how replication factors ensure data redundancy and how ScyllaDB maps partition keys to replica nodes for efficient reads and writes. You can find the complete lesson here. Primary Key, Partition Key, Clustering Key This lesson explains the structure and importance of primary keys in ScyllaDB, detailing their two components: the mandatory partition key and the optional clustering key. The partition key determines the data’s location across nodes, ensuring efficient querying, while the clustering key organizes rows within a partition. For queries to be efficient, the partition key must be specified to avoid full table scans. An example using pet data illustrates how rows are sorted within partitions by the clustering key (e.g., time), enabling precise and optimized data retrieval. Find the complete lesson here. Importance of Key Selection This video emphasizes the importance of choosing partition and clustering keys in ScyllaDB for optimal performance and data distribution. Partition keys should have high cardinality to ensure even data distribution across nodes and avoid issues like large or hot partitions. Examples of good keys include unique identifiers like user IDs, while low-cardinality keys like states or ages can lead to uneven load and inefficiency. Clustering keys should align with query patterns, considering the order of rows and prioritizing efficient retrieval, such as fetching recent data for time-sensitive applications. Strategic key selection prevents resource bottlenecks and enhances scalability. Learn more in the complete lesson. Data Modeling Lab Walkthrough (three parts) The new three-part video lesson focuses on key aspects of data modeling in ScyllaDB, emphasizing the design and use of primary keys. It demonstrates creating a cluster and tables using the CQL shell, highlighting how partition keys determine data location and efficient querying while showcasing different queries. Some tables use a Clustering key, which organizes data within partitions, enabling efficient range queries. It explains compound primary keys to enhance query flexibility. Next, an example of a different clustering key order (ascending or descending) is given. This enables query optimization and efficient retrieval of data. Throughout the lab walkthrough, different challenges are presented, as well as data modeling solutions to optimize performance, scalability, and resource utilization. You can watch the walkthrough here and also take the lab yourself. New in the Advanced Data Modeling Lesson Collections and Drivers The new lesson discusses advanced data modeling in ScyllaDB, focusing on collections (Sets, Lists, Maps, and User-defined types) to simplify models with multi-value fields like phone numbers or emails. It introduces token-aware and shard-aware drivers as optimizations to enhance query efficiency. Token-aware drivers allow clients to send requests directly to replica nodes, bypassing extra hops through coordinator nodes, while shard-aware clients target specific shards within replica nodes for improved performance. ScyllaDB supports drivers in multiple languages like Java, Python, and Go, along with compatibility with Cassandra drivers. An entire course on Drivers is also available. You can learn more in the complete lesson here. New in the ScyllaDB Operations Course Replica level Write/Read Path The lesson explains ScyllaDB’s read and write paths, focusing on how data is written to Memtables persisted as immutable SSTables. Because the SSTables are immutable, they are compacted periodically. Writes, including updates and deletes, are stored in a commit log before being flushed to SSTables. This ensures data consistency. For reads, a cache is used to optimize performance (also using bloom filters). Compaction merges SSTables to remove outdated data, maintain efficiency, and save storage. ScyllaDB offers different compaction strategies and you can choose the most suitable one based on your use case. Learn more in the full lesson. Tracing Demo The lesson provides a practical demonstration of ScyllaDB’s tracing using a three-node cluster. The tracing tool is showcased as a debugging aid to track request flows and replica responses. The demo highlights how data consistency levels influence when responses are sent back to clients and demonstrates high availability by successfully handling writes even when a node is down, provided the consistency requirements are met. You can find the complete lesson here.

Top Blogs of 2024: Comparisons, Caching & Database Internals

26 December 2024, 2:35 pm by ScyllaDB

Let’s look back at the top 10 ScyllaDB blog posts written this year – plus 10 “timeless classics” that continue to get attention. Before we start, thank you to all the community members who contributed to our blogs in various ways – from users sharing best practices at ScyllaDB Summit, to engineers explaining how they raised the bar for database performance, to anyone who has initiated or contributed to the discussion on HackerNews, Reddit, and other platforms. And if you have suggestions for 2025 blog topics, please share them with us on our socials. With no further ado, here are the most-read blog posts that we published in 2024…   We Compared ScyllaDB and Memcached and… We Lost? By Felipe Cardeneti Mendes Engineers behind ScyllaDB joined forces with Memcached maintainer dormando for an in-depth look at database and cache internals, and the tradeoffs in each. Read: We Compared ScyllaDB and Memcached and… We Lost? Related: Why Databases Cache, but Caches Go to Disk   Inside ScyllaDB’s Internal Cache By Pavel “Xemul” Emelyanov Why ScyllaDB completely bypasses the Linux cache during reads, using its own highly efficient row-based cache instead. Read: Inside ScyllaDB’s Internal Cache Related: Replacing Your Cache with ScyllaDB   Smooth Scaling: Why ScyllaDB Moved to “Tablets” Data Distribution By Avi Kivity The rationale behind ScyllaDB’s new “tablets” replication architecture, which builds upon a multiyear project to implement and extend Raft. Read: Smooth Scaling: Why ScyllaDB Moved to “Tablets” Data Distribution Related: ScyllaDB Fast Forward: True Elastic Scale   Rust vs. Zig in Reality: A (Somewhat) Friendly Debate By Cynthia Dunlop A (somewhat) friendly P99 CONF popup debate with Jarred Sumner (Bun.js), Pekka Enberg (Turso), and Glauber Costa (Turso) on ThePrimeagen’s stream. Read: Rust vs. Zig in Reality: A (Somewhat) Friendly Debate Related: P99 CONF on demand   Database Internals: Working with IO By Pavel “Xemul” Emelyanov Explore the tradeoffs of different Linux I/O methods and learn how databases can take advantage of a modern SSD’s unique characteristics. Read: Database Internals: Working with IO Related: Understanding Storage I/O Under Load   How We Implemented ScyllaDB’s “Tablets” Data Distribution By Avi Kivity How ScyllaDB implemented its new Raft-based tablets architecture, which enables teams to quickly scale out in response to traffic spikes. Read: How We Implemented ScyllaDB’s “Tablets” Data Distribution Related: Overcoming Distributed Databases Scaling Challenges with Tablets   How ShareChat Scaled their ML Feature Store 1000X without Scaling the Database By Ivan Burmistrov and Andrei Manakov How ShareChat engineers managed to meet their lofty performance goal without scaling the underlying database. Read: How ShareChat Scaled their ML Feature Store 1000X without Scaling the Database Related: ShareChat’s Path to High-Performance NoSQL with ScyllaDB   New Google Cloud Z3 Instances: Early Performance Benchmarks By Łukasz Sójka, Roy Dahan ScyllaDB had the privilege of testing Google Cloud’s brand new Z3 GCE instances in an early preview. We observed a 23% increase in write throughput, 24% for mixed workloads, and 14% for reads per vCPU – all at a lower cost compared to N2. Read:New Google Cloud Z3 Instances: Early Performance Benchmarks Related: A Deep Dive into ScyllaDB’s Architecture   Database Internals: Working with CPUs By Pavel “Xemul” Emelyanov Get a database engineer’s inside look at how the database interacts with the CPU…in this excerpt from the book, “Database Performance at Scale.” Read: Database Internals: Working with CPUs Related: Database Performance at Scale: A Practical Guide [Free Book]   Migrating from Postgres to ScyllaDB, with 349X Faster Query Processing By Dan Harris and Sebastian Vercruysse How Coralogix cut processing times from 30 seconds to 86 milliseconds with a PostgreSQL to ScyllaDB migration. Read: Migrating from Postgres to ScyllaDB, with 349X Faster Query Processing Related: NoSQL Migration Masterclass   Bonus: Top NoSQL Database Blogs From Years Past Many of the blogs published in previous years continued to resonate with the community. Here’s a rundown of 10 enduring favorites: How io_uring and eBPF Will Revolutionize Programming in Linux (Glauber Costa): How io_uring and eBPF will change the way programmers develop asynchronous interfaces and execute arbitrary code, such as tracepoints, more securely. [2020]   Benchmarking MongoDB vs ScyllaDB: Performance, Scalability & Cost (Dr. Daniel Seybold): Dr. Daniel Seybold shares how MongoDB and ScyllaDB compare on throughput, latency, scalability, and price-performance in this third-party benchmark by benchANT. [2023]   Introducing “Database Performance at Scale”: A Free, Open Source Book (Dor Laor): Introducing a new book that provides practical guidance for understanding the opportunities, trade-offs, and traps you might encounter while trying to optimize data-intensive applications for high throughput and low latency. [2023]   DynamoDB: When to Move Out (Felipe Cardeneti Mendes): A look at the top reasons why teams decide to leave DynamoDB: throttling, latency, item size limits, and limited flexibility…not to mention costs. [2023]   ScyllaDB vs MongoDB vs PostgreSQL: Tractian’s Benchmarking & Migration (João Pedro Voltani): TRACTIAN shares their comparison of ScyllaDB vs MongoDB and PostgreSQL, then provides an overview of their MongoDB to ScyllaDB migration process, challenges & results. [2023]   Benchmarking Apache Cassandra (40 Nodes) vs ScyllaDB (4 Nodes) (Juliusz Stasiewicz, Piotr Grabowski, Karol Baryla): We benchmarked Apache Cassandra on 40 nodes vs ScyllaDB on just 4 nodes. See how they stacked up on throughput, latency, and cost. [2022]   How Numberly Replaced Kafka with a Rust-Based ScyllaDB Shard-Aware Application (Alexys Jacob): How Numberly used Rust & ScyllaDB to replace Kafka, streamlining the way all its AdTech components send and track messages (whatever their form). [2023]   Async Rust in Practice: Performance, Pitfalls, Profiling (Piotr Sarna): How our engineers used flamegraphs to diagnose and resolve performance issues in our Tokio framework based Rust driver. [2022]   On Coordinated Omission (Ivan Prisyazhynyy): Your benchmark may be lying to you! Learn why coordinated omissions are a concern, and how we account for them in benchmarking ScyllaDB. [2021]   Why Disney+ Hotstar Replaced Redis and Elasticsearch with ScyllaDB Cloud (Cynthia Dunlop) – Get the inside perspective on how Disney+ Hotstar simplified its “continue watching” data architecture for scale. [2022]  

Innovative data compression for time series: An open source solution

4 December 2024, 9:17 am by Apache Cassandra - Instaclustr

Introduction

There’s no escaping the role that monitoring plays in our everyday lives. Whether it’s from monitoring the weather or the number of steps we take in a day, or computer systems to ever-popular IoT devices.  

Practically any activity can be monitored in one form or another these days. This generates increasing amounts of data to be pored over and analyzed–but storing all this data adds significant costs over time. Given this huge amount of data that only increases with each passing day, efficient compression techniques are crucial.  

Here at NetApp® Instaclustr we saw a great opportunity to improve the current compression techniques for our time series data. That’s why we created the Advanced Time Series Compressor (ATSC) in partnership with University of Canberra through the OpenSI initiative. 

ATSC is a groundbreaking compressor designed to address the challenges of efficiently compressing large volumes of time-series data. Internal test results with production data from our database metrics showed that ATSC would compress, on average of the dataset, ~10x more than LZ4 and ~30x more than the default Prometheus compression. Check out ATSC on GitHub. 

There are so many compressors already, so why develop another one? 

While other compression methods like LZ4, DoubleDelta, and ZSTD are lossless, most of our timeseries data is already lossy. Timeseries data can be lossy from the beginning due to under-sampling or insufficient data collection, or it can become lossy over time as metrics are rolled over or averaged. Because of this, the idea of a lossy compressor was born. 

ATSC is a highly configurable, lossy compressor that leverages the characteristics of time-series data to create function approximations. ATSC finds a fitting function and stores the parametrization of that function—no actual data from the original timeseries is stored. When the data is decompressed, it isn’t identical to the original, but it is still sufficient for the intended use. 

Here’s an example: for a temperature change metric—which mostly varies slowly (as do a lot of system metrics!)—instead of storing all the points that have a small change, we fit a curve (or a line) and store that curve/line achieving significant compression ratios. 

Image 1: ATSC data for temperature 

How does ATSC work? 

ATSC looks at the actual time series, in whole or in parts, to find how to better calculate a function that fits the existing data. For that, a quick statistical analysis is done, but if the results are inconclusive a sample is compressed with all the functions and the best function is selected.  

By default, ATSC will segment the data—this guarantees better local fitting, more and smaller computations, and less memory usage. It also ensures that decompression targets a specific block instead of the whole file. 

In each fitting frame, ATSC will create a function from a pre-defined set and calculate the parametrization of said function. 

ATSC currently uses one (per frame) of those following functions: 

• FFT (Fast Fourier Transforms) 
• Constant 
• Interpolation – Catmull-Rom 
• Interpolation – Inverse Distance Weight 

Image 2: Polynomial fitting vs. Fast-Fourier Transform fitting 

These methods allow ATSC to compress data with a fitting error within 1% (configurable!) of the original time-series.

For a more detailed insight into ATSC internals and operations check our paper! 

 Use cases for ATSC and results 

ATSC draws inspiration from established compression and signal analysis techniques, achieving compression ratios ranging from 46x to 880x with a fitting error within 1% of the original time-series. In some cases, ATSC can produce highly compressed data without losing any meaningful information, making it a versatile tool for various applications (please see use cases below). 

Some results from our internal tests comparing to LZ4 and normal Prometheus compression yielded the following results: 

Method	Compressed size (bytes)	Compression Ratio
Prometheus	454,778,552	1.33
LZ4	141,347,821	4.29
ATSC	14,276,544	42.47

Another characteristic is the trade-off between fast compression speed vs. slower compression speed. Compression is about 30x slower than decompression. It is expected that time-series are compressed once but decompressed several times. 

Image 3: A better fitting (purple) vs. a loose fitting (red). Purple takes twice as much space.

ATSC is versatile and can be applied in various scenarios where space reduction is prioritized over absolute precision. Some examples include: 

• Rolled-over time series: ATSC can offer significant space savings without meaningful loss in precision, such as metrics data that are rolled over and stored for long term. ATSC provides the same or more space savings but with minimal information loss. 
• Under-sampled time series: Increase sample rates without losing space. Systems that have very low sampling rates (30 seconds or more) and as such, it is very difficult to identify actual events. ATSC provides the space savings and keeps the information about the events. 
• Long, slow-moving data series: Ideal for patterns that are easy to fit, such as weather data. 
• Human visualization: Data meant for human analysis, with minimal impact on accuracy, such as historic views into system metrics (CPU, Memory, Disk, etc.)

Image 4: ATSC data (green) with an 88x compression vs. the original data (yellow)   

Using ATSC 

ATSC is written in Rust as and is available in GitHub. You can build and run yourself following these instructions. 

Future work 

Currently, we are planning to evolve ATSC in two ways (check our open issues): 

1. Adding features to the core compressor focused on these functionalities:
   • Frame expansion for appending new data to existing frames 
   • Dynamic function loading to add more functions without altering the codebase 
   • Global and per-frame error storage 
   • Improved error encoding 
2. Integrations with additional technologies (e.g. databases):
   • We are currently looking into integrating ASTC with ClickHouse® and Apache Cassandra® 

CREATE TABLE sensors_poly (   
    sensor_id UInt16,   
    location UInt32,
    timestamp DateTime,
    pressure Float64
CODEC(ATSC('Polynomial', 1)),
    temperature Float64 
CODEC(ATSC('Polynomial', 1)),
) 
ENGINE = MergeTree 
ORDER BY (sensor_id, location,
timestamp);

Image 5: Currently testing ClickHouse integration 

Sound interesting? Try it out and let us know what you think.  

ATSC represents a significant advancement in time-series data compression, offering high compression ratios with a configurable accuracy loss. Whether for long-term storage or efficient data visualization, ATSC is a powerful open source tool for managing large volumes of time-series data. 

But don’t just take our word for it—download and run it!  

Check our documentation for any information you need and submit ideas for improvements or issues you find using GitHub issues. We also have easy first issues tagged if you’d like to contribute to the project.   

Want to integrate this with another tool? You can build and run our demo integration with ClickHouse. 

The post Innovative data compression for time series: An open source solution appeared first on Instaclustr.

New cassandra_latest.yaml configuration for a top performant Apache Cassandra®

29 October 2024, 10:29 pm by Apache Cassandra - Instaclustr

Welcome to our deep dive into the latest advancements in Apache Cassandra® 5.0, specifically focusing on the cassandra_latest.yaml configuration that is available for new Cassandra 5.0 clusters.  

This blog post will walk you through the motivation behind these changes, how to use the new configuration, and the benefits it brings to your Cassandra clusters. 

Motivation 

The primary motivation for introducing cassandra_latest.yaml is to bridge the gap between maintaining backward compatibility and leveraging the latest features and performance improvements. The yaml addresses the following varying needs for new Cassandra 5.0 clusters: 

1. Cassandra Developers: who want to push new features but face challenges due to backward compatibility constraints. 
2. Operators: who prefer stability and minimal disruption during upgrades. 
3. Evangelists and New Users: who seek the latest features and performance enhancements without worrying about compatibility. 

Using cassandra_latest.yaml 

Using cassandra_latest.yaml is straightforward. It involves copying the cassandra_latest.yaml content to your cassandra.yaml or pointing the cassandra.config JVM property to the cassandra_latest.yaml file.  

This configuration is designed for new Cassandra 5.0 clusters (or those evaluating Cassandra), ensuring they get the most out of the latest features in Cassandra 5.0 and performance improvements. 

Key changes and features 

Key Cache Size 

• Old: Evaluated as a minimum from 5% of the heap or 100MB
• Latest: Explicitly set to 0

Impact: Setting the key cache size to 0 in the latest configuration avoids performance degradation with the new SSTable format. This change is particularly beneficial for clusters using the new SSTable format, which doesn’t require key caching in the same way as the old format. Key caching was used to reduce the time it takes to find a specific key in Cassandra storage. 

Commit Log Disk Access Mode 

• Old: Set to legacy
• Latest: Set to auto

Impact: The auto setting optimizes the commit log disk access mode based on the available disks, potentially improving write performance. It can automatically choose the best mode (e.g., direct I/O) depending on the hardware and workload, leading to better performance without manual tuning.

Memtable Implementation 

• Old: Skiplist-based
• Latest: Trie-based

Impact: The trie-based memtable implementation reduces garbage collection overhead and improves throughput by moving more metadata off-heap. This change can lead to more efficient memory usage and higher write performance, especially under heavy load.

create table … with memtable = {'class': 'TrieMemtable', … }

Memtable Allocation Type 

• Old: Heap buffers 
• Latest: Off-heap objects 

Impact: Using off-heap objects for memtable allocation reduces the pressure on the Java heap, which can improve garbage collection performance and overall system stability. This is particularly beneficial for large datasets and high-throughput environments. 

Trickle Fsync 

• Old: False 
• Latest: True 

Impact: Enabling trickle fsync improves performance on SSDs by periodically flushing dirty buffers to disk, which helps avoid sudden large I/O operations that can impact read latencies. This setting is particularly useful for maintaining consistent performance in write-heavy workloads. 

SSTable Format 

• Old: big 
• Latest: bti (trie-indexed structure) 

Impact: The new BTI format is designed to improve read and write performance by using a trie-based indexing structure. This can lead to faster data access and more efficient storage management, especially for large datasets. 

sstable:
  selected_format: bti
  default_compression: zstd
  compression:
    zstd:
      enabled: true
      chunk_length: 16KiB
      max_compressed_length: 16KiB

Default Compaction Strategy 

• Old: STCS (Size-Tiered Compaction Strategy) 
• Latest: Unified Compaction Strategy 

Impact: The Unified Compaction Strategy (UCS) is more efficient and can handle a wider variety of workloads compared to STCS. UCS can reduce write amplification and improve read performance by better managing the distribution of data across SSTables. 

default_compaction:
  class_name: UnifiedCompactionStrategy
  parameters:
    scaling_parameters: T4
    max_sstables_to_compact: 64
    target_sstable_size: 1GiB
    sstable_growth: 0.3333333333333333
    min_sstable_size: 100MiB

Concurrent Compactors 

• Old: Defaults to the smaller of the number of disks and cores
• Latest: Explicitly set to 8

Impact: Setting the number of concurrent compactors to 8 ensures that multiple compaction operations can run simultaneously, helping to maintain read performance during heavy write operations. This is particularly beneficial for SSD-backed storage where parallel I/O operations are more efficient. 

Default Secondary Index 

• Old: legacy_local_table
• Latest: sai

Impact: SAI is a new index implementation that builds on the advancements made with SSTable Storage Attached Secondary Index (SASI). Provide a solution that enables users to index multiple columns on the same table without suffering scaling problems, especially at write time. 

Stream Entire SSTables 

• Old: implicity set to True
• Latest: explicity set to True

Impact: When enabled, it permits Cassandra to zero-copy stream entire eligible, SSTables between nodes, including every component. This speeds up the network transfer significantly subject to throttling specified by

entire_sstable_stream_throughput_outbound

and

entire_sstable_inter_dc_stream_throughput_outbound

for inter-DC transfers. 

UUID SSTable Identifiers 

• Old: False
• Latest: True

Impact: Enabling UUID-based SSTable identifiers ensures that each SSTable has a unique name, simplifying backup and restore operations. This change reduces the risk of name collisions and makes it easier to manage SSTables in distributed environments. 

Storage Compatibility Mode 

• Old: Cassandra 4
• Latest: None

Impact: Setting the storage compatibility mode to none enables all new features by default, allowing users to take full advantage of the latest improvements, such as the new sstable format, in Cassandra. This setting is ideal for new clusters or those that do not need to maintain backward compatibility with older versions. 

Testing and validation 

The cassandra_latest.yaml configuration has undergone rigorous testing to ensure it works seamlessly. Currently, the Cassandra project CI pipeline tests both the standard (cassandra.yaml) and latest (cassandra_latest.yaml) configurations, ensuring compatibility and performance. This includes unit tests, distributed tests, and DTests. 

Future improvements 

Future improvements may include enforcing password strength policies and other security enhancements. The community is encouraged to suggest features that could be enabled by default in cassandra_latest.yaml. 

Conclusion 

The cassandra_latest.yaml configuration for new Cassandra 5.0 clusters is a significant step forward in making Cassandra more performant and feature-rich while maintaining the stability and reliability that users expect. Whether you are a developer, an operator professional, or an evangelist/end user, cassandra_latest.yaml offers something valuable for everyone. 

Try it out 

Ready to experience the incredible power of the cassandra_latest.yaml configuration on Apache Cassandra 5.0? Spin up your first cluster with a free trial on the Instaclustr Managed Platform and get started today with Cassandra 5.0!

The post New cassandra_latest.yaml configuration for a top performant Apache Cassandra® appeared first on Instaclustr.

Cassandra 5 Released! What's New and How to Try it

9 September 2024, 12:00 am by Posts on RustyRazorblade Consulting

Apache Cassandra 5.0 has officially landed! This highly anticipated release brings a range of new features and performance improvements to one of the most popular NoSQL databases in the world. Having recently hosted a webinar covering the major features of Cassandra 5.0, I’m excited to give a brief overview of the key updates and show you how to easily get hands-on with the latest release using easy-cass-lab.

You can grab the latest release on the Cassandra download page.

Instaclustr for Apache Cassandra® 5.0 Now Generally Available

5 September 2024, 11:18 pm by Apache Cassandra - Instaclustr

NetApp is excited to announce the general availability (GA) of Apache Cassandra® 5.0 on the Instaclustr Platform. This follows the release of the public preview in March.

NetApp was the first managed service provider to release the beta version, and now the Generally Available version, allowing the deployment of Cassandra 5.0 across the major cloud providers: AWS, Azure, and GCP, and on–premises.

Apache Cassandra has been a leader in NoSQL databases since its inception and is known for its high availability, reliability, and scalability. The latest version brings many new features and enhancements, with a special focus on building data-driven applications through artificial intelligence and machine learning capabilities.

Cassandra 5.0 will help you optimize performance, lower costs, and get started on the next generation of distributed computing by: 

• Helping you build AI/ML-based applications through Vector Search  
• Bringing efficiencies to your applications through new and enhanced indexing and processing capabilities 
• Improving flexibility and security 

With the GA release, you can use Cassandra 5.0 for your production workloads, which are covered by NetApp’s industry–leading SLAs. NetApp has conducted performance benchmarking and extensive testing while removing the limitations that were present in the preview release to offer a more reliable and stable version. Our GA offering is suitable for all workload types as it contains the most up-to-date range of features, bug fixes, and security patches.  

Support for continuous backups and private network add–ons is available. Currently, Debezium is not yet compatible with Cassandra 5.0. NetApp will work with the Debezium community to add support for Debezium on Cassandra 5.0 and it will be available on the Instaclustr Platform as soon as it is supported. 

Some of the key new features in Cassandra 5.0 include: 

• Storage-Attached Indexes (SAI): A highly scalable, globally distributed index for Cassandra databases. With SAI, column-level indexes can be added, leading to unparalleled I/O throughput for searches across different data types, including vectors. SAI also enables lightning-fast data retrieval through zero-copy streaming of indices, resulting in unprecedented efficiency.  
• Vector Search: This is a powerful technique for searching relevant content or discovering connections by comparing similarities in large document collections and is particularly useful for AI applications. It uses storage-attached indexing and dense indexing techniques to enhance data exploration and analysis.  
• Unified Compaction Strategy: This strategy unifies compaction approaches, including leveled, tiered, and time-windowed strategies. It leads to a major reduction in SSTable sizes. Smaller SSTables mean better read and write performance, reduced storage requirements, and improved overall efficiency.  
• Numerous stability and testing improvements: You can read all about these changes here. 

All these new features are available out-of-the-box in Cassandra 5.0 and do not incur additional costs.  

Our Development team has worked diligently to bring you a stable release of Cassandra 5.0. Substantial preparatory work was done to ensure you have a seamless experience with Cassandra 5.0 on the Instaclustr Platform. This includes updating the Cassandra YAML and Java environment and enhancing the monitoring capabilities of the platform to support new data types.  

We also conducted extensive performance testing and benchmarked version 5.0 with the existing stable Apache Cassandra 4.1.5 version. We will be publishing our benchmarking results shortly; the highlight so far is that Cassandra 5.0 improves responsiveness by reducing latencies by up to 30% during peak load times.  

Through our dedicated Apache Cassandra committer, NetApp has contributed to the development of Cassandra 5.0 by enhancing the documentation for new features like Vector Search (Cassandra-19030), enabling Materialized Views (MV) with only partition keys (Cassandra-13857), fixing numerous bugs, and contributing to the improvements for the unified compaction strategy feature, among many other things. 

Lifecycle Policy Updates 

As previously communicated, the project will no longer maintain Apache Cassandra 3.0 and 3.11 versions (full details of the announcement can be found on the Apache Cassandra website).

To help you transition smoothly, NetApp will provide extended support for these versions for an additional 12 months. During this period, we will backport any critical bug fixes, including security patches, to ensure the continued security and stability of your clusters. 

Cassandra 3.0 and 3.11 versions will reach end-of-life on the Instaclustr Managed Platform within the next 12 months. We will work with you to plan and upgrade your clusters during this period.  

Additionally, the Cassandra 5.0 beta version and the Cassandra 5.0 RC2 version, which were released as part of the public preview, are now end-of-life You can check the lifecycle status of different Cassandra application versions here.  

You can read more about our lifecycle policies on our website.  

Getting Started 

Upgrading to Cassandra 5.0 will allow you to stay current and start taking advantage of its benefits. The Instaclustr by NetApp Support team is ready to help customers upgrade clusters to the latest version.  

• Wondering if it’s possible to upgrade your workloads from Cassandra 3.x to Cassandra 5.0? Find the answer to this and other similar questions in this detailed blog.
• Click here to read about Storage Attached Indexes in Apache Cassandra 5.0.
• Learn about 4 new Apache Cassandra 5.0 features to be excited about. 
• Click here to learn what you need to know about Apache Cassandra 5.0. 

Why Choose Apache Cassandra on the Instaclustr Managed Platform? 

NetApp strives to deliver the best of supported applications. Whether it’s the latest and newest application versions available on the platform or additional platform enhancements, we ensure a high quality through thorough testing before entering General Availability.  

NetApp customers have the advantage of accessing the latest versions—not just the major version releases but also minor version releases—so that they can benefit from any new features and are protected from any vulnerabilities.  

Don’t have an Instaclustr account yet? Sign up for a trial or reach out to our Sales team and start exploring Cassandra 5.0.  

With more than 375 million node hours of management experience, Instaclustr offers unparalleled expertise. Visit our website to learn more about the Instaclustr Managed Platform for Apache Cassandra.  

If you would like to upgrade your Apache Cassandra version or have any issues or questions about provisioning your cluster, please contact Instaclustr Support at any time.  

The post Instaclustr for Apache Cassandra® 5.0 Now Generally Available appeared first on Instaclustr.

Apache Cassandra® 5.0: Behind the Scenes

4 September 2024, 3:53 am by Apache Cassandra - Instaclustr

Here at NetApp, our Instaclustr product development team has spent nearly a year preparing for the release of Apache Cassandra 5.  

Starting with one engineer tinkering at night with the Apache Cassandra 5 Alpha branch, and then up to 5 engineers working on various monitoring, configuration, testing and functionality improvements to integrate the release with the Instaclustr Platform.  

It’s been a long journey to the point we are at today, offering Apache Cassandra 5 Release Candidate 1 in public preview on the Instaclustr Platform. 

Note: the Instaclustr team has a dedicated open source committer to the Apache Cassandra project. His changes are not included in this document as there were too many for us to include here. Instead, this blog primarily focuses on the engineering effort to release Cassandra 5.0 onto the Instaclustr Managed Platform. 

August 2023: The Beginning

We began experimenting with the Apache Cassandra 5 Alpha 1 branches using our build systems. There were several tools we built into our Apache Cassandra images that were not working at this point, but we managed to get a node to start even though it immediately crashed with errors.  

One of our early achievements was identifying and fixing a bug that impacted our packaging solution; this resulted in a small contribution to the project allowing Apache Cassandra to be installed on Debian systems with non-OpenJDK Java. 

September 2023: First Milestone 

The release of the Alpha 1 version allowed us to achieve our first running Cassandra 5 cluster in our development environments (without crashing!).  

Basic core functionalities like user creation, data writing, and backups/restores were tested successfully. However, several advanced features, such as repair and replace tooling, monitoring, and alerting were still untested.  

At this point we had to pause our Cassandra 5 efforts to focus on other priorities and planned to get back to testing Cassandra 5 after Alpha 2 was released. 

November 2023:  Further Testing and Internal Preview 

The project released Alpha 2. We repeated the same build and test we did on alpha 1. We also tested some more advanced procedures like cluster resizes with no issues.  

We also started testing with some of the new 5.0 features: Vector Data types and Storage-Attached Indexes (SAI), which resulted in another small contribution.  

We launched Apache Cassandra 5 Alpha 2 for internal preview (basically for internal users). This allowed the wider Instaclustr team to access and use the Alpha on the platform.  

During this phase we found a bug in our metrics collector when vectors were encountered that ended up being a major project for us. 

If you see errors like the below, it’s time for a Java Cassandra driver upgrade to 4.16 or newer: 

java.lang.IllegalArgumentException: Could not parse type name vector<float, 5>  
Nov 15 22:41:04 ip-10-0-39-7 process[1548]: at com.datastax.driver.core.DataTypeCqlNameParser.parse(DataTypeCqlNameParser.java:233)  
Nov 15 22:41:04 ip-10-0-39-7 process[1548]: at com.datastax.driver.core.TableMetadata.build(TableMetadata.java:311)
Nov 15 22:41:04 ip-10-0-39-7 process[1548]: at com.datastax.driver.core.SchemaParser.buildTables(SchemaParser.java:302)
Nov 15 22:41:04 ip-10-0-39-7 process[1548]: at com.datastax.driver.core.SchemaParser.refresh(SchemaParser.java:130)
Nov 15 22:41:04 ip-10-0-39-7 process[1548]: at com.datastax.driver.core.ControlConnection.refreshSchema(ControlConnection.java:417)  
Nov 15 22:41:04 ip-10-0-39-7 process[1548]: at com.datastax.driver.core.ControlConnection.refreshSchema(ControlConnection.java:356)  
<Rest of stacktrace removed for brevity>

December 2023: Focus on new features and planning 

As the project released Beta 1, we began focusing on the features in Cassandra 5 that we thought were the most exciting and would provide the most value to customers. There are a lot of awesome new features and changes, so it took a while to find the ones with the largest impact.  

 The final list of high impact features we came up with was: 

• A new data type – Vectors 
• Trie memtables/Trie Indexed SSTables (BTI Formatted SStables) 
• Storage-Attached Indexes (SAI) 
• Unified Compaction Strategy 

A major new feature we considered deploying was support for JDK 17. However, due to its experimental nature, we have opted to postpone adoption and plan to support running Apache Cassandra on JDK 17 when it’s out of the experimentation phase. 

Once the holiday season arrived, it was time for a break, and we were back in force in February next year. 

February 2024: Intensive testing 

In February, we released Beta 1 into internal preview so we could start testing it on our Preproduction test environments. As we started to do more intensive testing, we discovered issues in the interaction with our monitoring and provisioning setup. 

We quickly fixed the issues identified as showstoppers for launching Cassandra 5. By the end of February, we initiated discussions about a public preview release. We also started to add more resourcing to the Cassandra 5 project. Up until now, only one person was working on it.  

Next, we broke down the work we needed to do.  This included identifying monitoring agents requiring upgrade and config defaults that needed to change. 

From this point, the project split into 3 streams of work: 

1. Project Planning – Deciding how all this work gets pulled together cleanly, ensuring other work streams have adequate resourcing to hit their goals, and informing product management and the wider business of what’s happening.  
2. Configuration Tuning – Focusing on the new features of Apache Cassandra to include, how to approach the transition to JDK 17, and how to use BTI formatted SSTables on the platform.  
3. Infrastructure Upgrades – Identifying what to upgrade internally to handle Cassandra 5, including Vectors and BTI formatted SSTables. 

A Senior Engineer was responsible for each workstream to ensure planned timeframes were achieved. 

March 2024: Public Preview Release 

In March, we launched Beta 1 into public preview on the Instaclustr Managed Platform. The initial release did not contain any opt in features like Trie indexed SSTables. 

However, this gave us a consistent base to test in our development, test, and production environments, and proved our release pipeline for Apache Cassandra 5 was working as intended. This also gave customers the opportunity to start using Apache Cassandra 5 with their own use cases and environments for experimentation.  

See our public preview launch blog for further details. 

There was not much time to celebrate as we continued working on infrastructure and refining our configuration defaults. 

April 2024: Configuration Tuning and Deeper Testing 

The first configuration updates were completed for Beta 1, and we started performing deeper functional and performance testing. We identified a few issues from this effort and remediated. This default configuration was applied for all Beta 1 clusters moving forward.  

This allowed users to start testing Trie Indexed SSTables and Trie memtables in their environment by default. 

"memtable": 
  { 
    "configurations": 
      { 
        "skiplist": 
          { 
            "class_name": "SkipListMemtable" 
          }, 
        "sharded": 
          { 
            "class_name": "ShardedSkipListMemtable" 
          }, 
        "trie": 
          { 
            "class_name": "TrieMemtable" 
          }, 
        "default": 
          { 
            "inherits": "trie" 
          } 
      } 
  }, 
"sstable": 
  { 
    "selected_format": "bti" 
  }, 
"storage_compatibility_mode": "NONE",

The above graphic illustrates an Apache Cassandra YAML configuration where BTI formatted sstables are used by default (which allows Trie Indexed SSTables) and defaults use of Trie for memtables.  You can override this per table: 

CREATE TABLE test WITH memtable = {‘class’ : ‘ShardedSkipListMemtable’};

Note that you need to set storage_compatibility_mode to NONE to use BTI formatted sstables. See Cassandra documentation for more information. 

You can also reference the cassandra_latest.yaml  file for the latest settings (please note you should not apply these to existing clusters without rigorous testing). 

May 2024: Major Infrastructure Milestone 

We hit a very large infrastructure milestone when we released an upgrade to some of our core agents that were reliant on an older version of the Apache Cassandra Java driver. The upgrade to version 4.17 allowed us to start supporting vectors in certain keyspace level monitoring operations.  

At the time, this was considered to be the riskiest part of the entire project as we had 1000s of nodes to upgrade across may different customer environments. This upgrade took a few weeks, finishing in June. We broke the release up into 4 separate rollouts to reduce the risk of introducing issues into our fleet, focusing on single key components in our architecture in each release. Each release had quality gates and tested rollback plans, which in the end were not needed. 

June 2024: Successful Rollout New Cassandra Driver 

The Java driver upgrade project was rolled out to all nodes in our fleet and no issues were encountered. At this point we hit all the major milestones before Release Candidates became available. We started to look at the testing systems to update to Apache Cassandra 5 by default. 

July 2024: Path to Release Candidate 

We upgraded our internal testing systems to use Cassandra 5 by default, meaning our nightly platform tests began running against Cassandra 5 clusters and our production releases will smoke test using Apache Cassandra 5. We started testing the upgrade path for clusters from 4.x to 5.0. This resulted in another small contribution to the Cassandra project.  

The Apache Cassandra project released Apache Cassandra 5 Release Candidate 1 (RC1), and we launched RC1 into public preview on the Instaclustr Platform. 

The Road Ahead to General Availability 

We’ve just launched Apache Cassandra 5 Release Candidate 1 (RC1) into public preview, and there’s still more to do before we reach General Availability for Cassandra 5, including: 

• Upgrading our own preproduction Apache Cassandra for internal use to Apache Cassandra 5 Release Candidate 1. This means we’ll be testing using our real-world use cases and testing our upgrade procedures on live infrastructure. 

At Launch: 

When Apache Cassandra 5.0 launches, we will perform another round of testing, including performance benchmarking. We will also upgrade our internal metrics storage production Apache Cassandra clusters to 5.0, and, if the results are satisfactory, we will mark the release as generally available for our customers. We want to have full confidence in running 5.0 before we recommend it for production use to our customers.  

For more information about our own usage of Cassandra for storing metrics on the Instaclustr Platform check out our series on Monitoring at Scale.  

What Have We Learned From This Project? 

• Releasing limited, small and frequent changes has resulted in a smooth project, even if sometimes frequent releases do not feel smooth. Some thoughts: 
  • Releasing to a small subset of internal users allowed us to take risks and break things more often so we could learn from our failures safely.
  • Releasing small changes allowed us to more easily understand and predict the behaviour of our changes: what to look out for in case things went wrong, how to more easily measure success, etc. 
  • Releasing frequently built confidence within the wider Instaclustr team, which in turn meant we would be happier taking more risks and could release more often.  
• Releasing to internal and public preview helped create momentum within the Instaclustr business and teams:  
  • This turned the Apache Cassandra 5.0 release from something that “was coming soon and very exciting” to “something I can actually use.”
• Communicating frequently, transparently, and efficiently is the foundation of success:  
  • We used a dedicated Slack channel (very creatively named #cassandra-5-project) to discuss everything. 
  • It was quick and easy to go back to see why we made certain decisions or revisit them if needed. This had a bonus of allowing a Lead Engineer to write a blog post very quickly about the Cassandra 5 project. 

This has been a long–running but very exciting project for the entire team here at Instaclustr. The Apache Cassandra community is on the home stretch for this massive release, and we couldn’t be more excited to start seeing what everyone will build with it.  

You can sign up today for a free trial and test Apache Cassandra 5 Release Candidate 1 by creating a cluster on the Instaclustr Managed Platform.  

More Readings 

• The Top 5 Questions We’re Asked about Apache Cassandra 5.0 
• Vector Search in Apache Cassandra 5.0 
• How Does Data Modeling Change in Apache Cassandra 5.0?

 

The post Apache Cassandra® 5.0: Behind the Scenes appeared first on Instaclustr.

Will Your Cassandra Database Project Succeed?: The New Stack

3 September 2024, 12:45 am by Apache Cassandra - Instaclustr

Open source Apache Cassandra® continues to stand out as an enterprise-proven solution for organizations seeking high availability, scalability and performance in a NoSQL database. (And hey, the brand-new 5.0 version is only making those statements even more true!) There’s a reason this database is trusted by some of the world’s largest and most successful companies.

That said, effectively harnessing the full spectrum of Cassandra’s powerful advantages can mean overcoming a fair share of operational complexity. Some folks will find a significant learning curve, and knowing what to expect is critical to success. In my years of experience working with Cassandra, it’s when organizations fail to anticipate and respect these challenges that they set the stage for their Cassandra projects to fall short of expectations.

Let’s look at the key areas where strong project management and following proven best practices will enable teams to evade common pitfalls and ensure a Cassandra implementation is built strong from Day 1.

Accurate Data Modeling Is a Must

Cassandra projects require a thorough understanding of its unique data model principles. Teams that approach Cassandra like a relationship database are unlikely to model data properly. This can lead to poor performance, excessive use of secondary indexes and significant data consistency issues.

On the other hand, teams that develop familiarity with Cassandra’s specific NoSQL data model will understand the importance of including partition keys, clustering keys and denormalization. These teams will know to closely analyze query and data access patterns associated with their applications and know how to use that understanding to build a Cassandra data model that matches their application’s needs step for step.

The post Will Your Cassandra Database Project Succeed?: The New Stack appeared first on Instaclustr.

Use Your Data in LLMs With the Vector Database You Already Have: The New Stack

6 August 2024, 7:08 am by Apache Cassandra - Instaclustr

Open source vector databases are among the top options out there for AI development, including some you may already be familiar with or even have on hand.

Vector databases allow you to enhance your LLM models with data from your internal data stores. Prompting the LLM with local, factual knowledge can allow you to get responses tailored to what your organization already knows about the situation. This reduces “AI hallucination” and improves relevance.

You can even ask the LLM to add references to the original data it used in its answer so you can check yourself. No doubt vendors have reached out with proprietary vector database solutions, advertised as a “magic wand” enabling you to assuage any AI hallucination concerns.

But, ready for some good news?

If you’re already using Apache Cassandra 5.0, OpenSearch or PostgreSQL, your vector database success is already primed. That’s right: There’s no need for costly proprietary vector database offerings. If you’re not (yet) using these free and fully open source database technologies, your generative AI aspirations are a good time to migrate — they are all enterprise-ready and avoid the pitfalls of proprietary systems.

For many enterprises, these open source vector databases are the most direct route to implementing LLMs — and possibly leveraging retrieval augmented generation (RAG) — that deliver tailored and factual AI experiences.

Vector databases store embedding vectors, which are lists of numbers representing spatial coordinates corresponding to pieces of data. Related data will have closer coordinates, allowing LLMs to make sense of complex and unstructured datasets for features such as generative AI responses and search capabilities.

RAG, a process skyrocketing in popularity, involves using a vector database to translate the words in an enterprise’s documents into embeddings to provide highly efficient and accurate querying of that documentation via LLMs.

The post Use Your Data in LLMs With the Vector Database You Already Have: The New Stack appeared first on Instaclustr.

easy-cass-lab v5 released

5 August 2024, 12:00 am by Posts on RustyRazorblade Consulting

I’ve got some fun news to start the week off for users of easy-cass-lab: I’ve just released version 5. There are a number of nice improvements and bug fixes in here that should make it more enjoyable, more useful, and lay groundwork for some future enhancements.

• When the cluster starts, we wait for the storage service to reach NORMAL state, then move to the next node. This is in contrast to the previous behavior where we waited for 2 minutes after starting a node. This queries JMX directly using Swiss Java Knife and is more reliable than the 2-minute method. Please see packer/bin-cassandra/wait-for-up-normal to read through the implementation.
• Trunk now works correctly. Unfortunately, AxonOps doesn’t support trunk (5.1) yet, and using the agent was causing a startup error. You can test trunk out, but for now the AxonOps integration is disabled.
• Added a new repl mode. This saves keystrokes and provides some auto-complete functionality and keeps SSH connections open. If you’re going to do a lot of work with ECL this will help you be a little more efficient. You can try this out with ecl repl.
• Power user feature: Initial support for profiles in AWS regions other than us-west-2. We only provide AMIs for us-west-2, but you can now set up a profile in an alternate region, and build the required AMIs using easy-cass-lab build-image. This feature is still under development and requires using an easy-cass-lab build from source. Credit to Jordan West for contributing this work.
• Power user feature: Support for multiple profiles. Setting the EASY_CASS_LAB_PROFILE environment variable allows you to configure alternate profiles. This is handy if you want to use multiple regions or have multiple organizations.
• The project now uses Kotlin instead of Groovy for Gradle configuration.
• Updated Gradle to 8.9.
• When using the list command, don’t show the alias “current”.
• Project cleanup, remove old unused pssh, cassandra build, and async profiler subprojects.

The release has been released to the project’s GitHub page and to homebrew. The project is largely driven by my own consulting needs and for my training. If you’re looking to have some features prioritized please reach out, and we can discuss a consulting engagement.

Who Did What to That and When? Exploring the User Actions Feature

31 July 2024, 3:45 am by Apache Cassandra - Instaclustr

NetApp recently released the user actions feature on the Instaclustr Managed Platform, allowing customers to search for user actions recorded against their accounts and organizations. We record over 100 different types of actions, with detailed descriptions of what was done, by whom, to what, and at what time. 

This provides customers with visibility into the actions users are performing on their linked accounts. NetApp has always collected this information in line with our security and compliance policies, but now, all important changes to your managed cluster resources have self-service access from the Console and the APIs.

In the past, this information was accessible only through support tickets when important questions such as “Who deleted my cluster?” and “When was the firewall rule removed from my cluster?” needed answers. This feature adds more self-discoverability of what your users are doing and what our support staff are doing to keep your clusters healthy. 

This blog post provides a detailed walkthrough of this new feature at a moderate level of technical detail, with the hope of encouraging you to explore and better find the actions you are looking for. 

For this blog, I’ve created two Apache Cassandra® clusters in one account and performed some actions on each. I’ve also created an organization linked to this account and performed some actions on that. This will allow a full example UI to be shown and demonstrate the type of “stories” that can emerge from typical operations via user actions. 

Introducing Global Directory 

During development, we decided to consolidate the other global account pages into a new centralized location, which we are calling the “Directory”.  

This Directory provides you with the consolidated view of all organizations and accounts that you have access to, collecting global searches and account functions into a view that does not have a “selected cluster” context (i.e., global).  For more information on how Organizations, Accounts and Clusters relate to each other, check out this blog.

Organizations serve as an efficient method to consolidate all associated accounts into a single unified, easily accessible location. They introduce an extra layer to the permission model, facilitating the management and sharing of information such as contact and billing details. They also streamline the process of Single Sign-On (SSO) and account creation. 

Let’s log in and click on the new button: 

This will take us to the new directory landing page: 

Here, you will find two types of global searches: accounts and user actions, as well as account creation. Selecting the new “User Actions” item will take us to the new page. You can also navigate to these directory pages directly from the top right ‘folder’ menu:

User Action Search Page: Walkthrough 

This is the new page we land on if we choose to search for user actions: 

When you first enter, it finds the last page of actions that happened in the accounts and organizations you have access to. It will show both organization and account actions on a single consolidated page, even though they are slightly different in nature. 

*Note: The accessible accounts and organisations are defined as those you are linked to as

CLUSTER_ADMIN

or

OWNER

*TIP: If you don’t want an account user to see user actions, give the

READ_ONLY

access. 

You may notice a brief progress bar display as the actions are retrieved. At the time of writing, we have recorded nearly 100 million actions made by our customers over a 6-month period.  

From here, you can increase the number of actions shown on each page and page through the results. Sorting is not currently supported on the actions table, but it is something we will be looking to add in the future. For each action found, the table will display: 

• Action: What happened to your account (or organization)? There are over 100 tracked kinds of actions recorded. 
• Domain: The specific account or organization name of the action targeted. 
• Description: An expanded description of what happened, using context captured at the time of action. Important values are highlighted between square brackets, and the copy button will copy the first one into the clipboard. 
• User: The user who performed the action, typically using the console/ APIs or Terraform provider, but it can also be triggered by “Instaclustr Support” using our admin tools.
  • For those actions marked with user “Instaclustr Support”, please reach out to support for more information about those actions we’ve taken on your behalf or visit https://support.instaclustr.com/hc/en-us. 
• Local time: The action time from your local web browser’s perspective. 

Additionally, for those who prefer programmatic access, the user action feature is fully accessible via our APIs, allowing for automation and integration into your existing workflows. Please visit our API documentation page here for more details.  

Basic (super-search) Mode 

Let’s say we only care about the “LeagueOfNations” organization domain; we can type ‘League’ and then click Search: 

The name patterns are simple partial string patterns we look for as being ’contained’ within the name, such as ”Car” in ”Carlton”. These are case insensitive. They are not (yet!) general regular expressions.

Advanced “find a needle” Search Mode 

Sometimes, searching by names is not precise enough; you may want to provide more detailed search criteria, such as time ranges or narrowing down to specific clusters or kinds of actions. Expanding the “Advanced Search” section will switch the page to a more advanced search criteria form, disabling the basic search area and its criteria. 

Let’s say we only want to see the “Link Account” actions over the last week: 

We select it from the actions multi-chip selector using the cursor (we could also type it and allow autocomplete to kick in). Hitting search will give you your needle time to go chase that Carl guy down and ask why he linked that darn account: 

The available criteria fields are as follows (additive in nature): 

• Action: the kinds of actions, with a bracketed count of their frequency over the current criteria; if empty, all are included. 
• Account: The account name of interest OR its UUID can be useful to narrow the matches to only a specific account. It’s also useful when user, organization, and account names share string patterns, which makes the super-search less precise. 
• Organization: the organization name of interest or its UUID. 
• User: the user who performed the action. 
• Description: matches against the value of an expanded description variable. This is useful because most actions mention the ‘target’ of the action, such as cluster-id, in the expanded description. 
• Starting At: match actions starting from this time cannot be older than 12 months ago. 
• Ending At: match actions up until this time. 

Bonus Feature: Cluster Actions 

While it’s nice to have this new search page, we wanted to build a higher-order question on top of it: What has happened to my cluster?  

The answer can be found on the details tab of each cluster. When clicked on, it will take you directly to the user actions page with appropriate criteria to answer the question. 

* TIP: we currently support entry into this view with a

descriptionFormat queryParam

allowing you to save bookmarks to particular action ‘targets’. Further

queryParams

may be supported in the future for the remaining criteria: https://console2.instaclustr.com/global/searches/user-action?descriptionContextPattern=acde7535-3288-48fa-be64-0f7afe4641b3

Clicking this provides you the answer:

Future Thoughts 

There are some future capabilities we will look to add, including the ability to subscribe to webhooks that trigger on some criteria. We would also like to add the ability to generate reports against a criterion or to run such things regularly and send them via email. Let us know what other feature improvements you would like to see! 

Conclusion 

This new capability allows customers to search for user actions directly without contacting support. It also provides improved visibility and auditing of what’s been changing on their clusters and who’s been making those changes. We hope you found this interesting and welcome any feedback for “higher-order” types of searches you’d like to see built on top of this new feature. What kind of common questions about user actions can you think of? 

If you have any questions about this feature, please contact Instaclustr Support at any time.  If you are not a current Instaclustr customer and you’re interested to learn more, register for a free trial and spin up your first cluster for free!

 

The post Who Did What to That and When? Exploring the User Actions Feature appeared first on Instaclustr.

easy-cass-lab updated with Cassandra 5.0 RC-1 Support

23 July 2024, 12:00 am by Posts on RustyRazorblade Consulting

I’m excited to announce that the latest version of easy-cass-lab now supports Cassandra 5.0 RC-1, which was just made available last week! This update marks a significant milestone, providing users with the ability to test and experiment with the newest Cassandra 5.0 features in a simplified manner. This post will walk you through how to set up a cluster, SSH in, and run your first stress test.

For those new to easy-cass-lab, it’s a tool designed to streamline the setup and management of Cassandra clusters in AWS, making it accessible for both new and experienced users. Whether you’re running tests, developing new features, or just exploring Cassandra, easy-cass-lab is your go-to tool.

easy-cass-lab now available in Homebrew

18 July 2024, 12:00 am by Posts on RustyRazorblade Consulting

I’m happy to share some exciting news for all Cassandra enthusiasts! My open source project, easy-cass-lab, is now installable via a homebrew tap. This powerful tool is designed to make testing any major version of Cassandra (or even builds that haven’t been released yet) a breeze, using AWS. A big thank-you to Jordan West who took the time to make this happen!

What is easy-cass-lab?

easy-cass-lab is a versatile testing tool for Apache Cassandra. Whether you’re dealing with the latest stable releases or experimenting with unreleased builds, easy-cass-lab provides a seamless way to test and validate your applications. With easy-cass-lab, you can ensure compatibility and performance across different Cassandra versions, making it an essential tool for developers and system administrators. easy-cass-lab is used extensively for my consulting engagements, my training program, and to evaluate performance patches destined for open source Cassandra. Here are a few examples:

Cassandra Training Signups For July and August Are Open!

25 April 2024, 12:00 am by Posts on RustyRazorblade Consulting

I’m pleased to announce that I’ve opened training signups for Operator Excellence to the public for July and August. If you’re interested in stepping up your game as a Cassandra operator, this course is for you. Head over to the training page to find out more and sign up for the course.

Streaming My Sessions With Cassandra 5.0

28 February 2024, 12:00 am by Posts on RustyRazorblade Consulting

As a long time participant with the Cassandra project, I’ve witnessed firsthand the evolution of this incredible database. From its early days to the present, our journey has been marked by continuous innovation, challenges, and a relentless pursuit of excellence. I’m thrilled to share that I’ll be streaming several working sessions over the next several weeks as I evaluate the latest builds and test out new features as we move toward the 5.0 release.

Streaming Cassandra Workloads and Experiments

26 February 2024, 12:00 am by Posts on RustyRazorblade Consulting

Streaming

In the world of software engineering, especially within the realm of distributed systems, continuous learning and experimentation are not just beneficial; they’re essential. As a software engineer with a focus on distributed systems, particularly Apache Cassandra, I’ve taken this ethos to heart. My journey has led me to not only explore the intricacies of Cassandra’s distributed architecture but also to share my experiences and findings with a broader audience. This is why my YouTube channel has become an active platform where I stream at least once a week, engaging with viewers through coding sessions, trying new approaches, and benchmarking different Cassandra workloads.

Live Streaming On Tuesdays

1 February 2024, 12:00 am by Posts on RustyRazorblade Consulting

As I promised in December, I redid my presentation from the Cassandra Summit 2023 on a live stream. You can check it out at the bottom of this post.

Going forward, I’ll be live-streaming on Tuesdays at 10AM Pacific on my YouTube channel.

Next week I’ll be taking a look at tlp-stress, which is used by the teams at some of the biggest Cassandra deployments in the world to benchmark their clusters. You can find that here.

Cassandra Summit Recap: Performance Tuning and Cassandra Training

11 January 2024, 12:00 am by Posts on RustyRazorblade Consulting

Hello, friends in the Apache Cassandra community!

I recently had the pleasure of speaking at the Cassandra Summit in San Jose. Unfortunately, we ran into an issue with my screen refusing to cooperate with the projector, so my slides were pretty distorted and hard to read. While the talk is online, I think it would be better to have a version with the right slides as well as a little more time. I’ve decided to redo the entire talk via a live stream on YouTube. I’m scheduling this for 10am PST on Wednesday, January 17 on my YouTube channel. My original talk was done in 30 minute slot, this will be a full hour, giving plenty of time for Q&A.

Cassandra Summit, YouTube, and a Mailing List

6 December 2023, 12:00 am by Posts on RustyRazorblade Consulting

I am thrilled to share some significant updates and exciting plans with my readers and the Cassandra community. As we draw closer to the end of the year, I’m preparing for an important speaking engagement and mapping out a year ahead filled with engaging and informative activities.

Cassandra Summit Presentation: Mastering Performance Tuning

I am honored to announce that I will be speaking at the upcoming Cassandra Summit. My talk, titled “Cassandra Performance Tuning Like You’ve Been Doing It for Ten Years,” is scheduled for December 13th, from 4:10 pm to 4:40 pm. This session aims to equip attendees with advanced insights and practical skills for optimizing Cassandra’s performance, drawing from a decade’s worth of experience in the field. Whether you’re new to Cassandra or a seasoned user, this talk will provide valuable insights to enhance your database management skills.

Uncover Cassandra's Throughput Boundaries with the New Adaptive Scheduler in tlp-stress

31 October 2023, 12:00 am by Posts on RustyRazorblade Consulting

Introduction

Apache Cassandra remains the preferred choice for organizations seeking a massively scalable NoSQL database. To guarantee predictable performance, Cassandra administrators and developers rely on benchmarking tools like tlp-stress, nosqlbench, and ndbench to help them discover their cluster’s limits. In this post, we will explore the latest advancements in tlp-stress, highlighting the introduction of the new Adaptive Scheduler. This brand-new feature allows users to more easily uncover the throughput boundaries of Cassandra clusters while remaining within specific read and write latency targets. First though, we’ll take a brief look at the new workload designed to stress test the new Storage Attached Indexes feature coming in Cassandra 5.

AxonOps Review - An Operations Platform for Apache Cassandra

24 October 2023, 12:00 am by Posts on RustyRazorblade Consulting

Note: Before we dive into this review of AxonOps and their offerings, it’s important to note that this blog post is part of a paid engagement in which I provided product feedback. AxonOps had no influence or say over the content of this post and did not have access to it prior to publishing.

In the ever-evolving landscape of data management, companies are constantly seeking solutions that can simplify the complexities of database operations. One such player in the market is AxonOps, a company that specializes in providing tooling for operating Apache Cassandra.

Benchmarking Apache Cassandra with tlp-stress

20 July 2023, 12:00 am by Posts on RustyRazorblade Consulting

This post will introduce you to tlp-stress, a tool for benchmarking Apache Cassandra. I started tlp-stress back when I was working at The Last Pickle. At the time, I was spending a lot of time helping teams identify the root cause of performance issues and needed a way of benchmarking. I found cassandra-stress to be difficult to use and configure, so I ended up writing my own tool that worked in a manner that I found to be more useful. If you’re looking for a tool to assist you in benchmarking Cassandra, and you’re looking to get started quickly, this might be the right tool for you.

Back to Consulting!

22 March 2023, 12:00 am by Posts on RustyRazorblade Consulting

Saying “it’s been a while since I wrote anything here” would be an understatement, but I’m back, with a lot to talk about in the upcoming months.

First off - if you’re not aware, I continued writing, but on The Last Pickle blog. There’s quite a few posts there, here are the most interesting ones:

• 14 Things To Do When Setting Up a New Cassandra Cluster
• Apache Cassandra Performance Tuning - Compression with Mixed Workloads
• Garbage Collection Tuning for Apache Cassandra
• Analyzing Cassandra Performance with Flame Graphs
• Cassandra Time Series Data Modeling For Massive Scale

Now the fun part - I’ve spent the last 3 years at Apple, then Netflix, neither of which gave me much time to continue my writing. As of this month, I’m officially no longer at Netflix and have started Rustyrazorblade Consulting!

Building a 100% ScyllaDB Shard-Aware Application Using Rust

15 February 2023, 12:00 am by Ultrabug

Building a 100% ScyllaDB Shard-Aware Application Using Rust

I wrote a web transcript of the talk I gave with my colleagues Joseph and Yassir at [Scylla Su...

Learning Rust the hard way for a production Kafka+ScyllaDB pipeline

21 February 2022, 12:00 am by Ultrabug

Learning Rust the hard way for a production Kafka+ScyllaDB pipeline

This is the web version of the talk I gave at [Scylla Summit 2022](https://www.scyllad...

On Scylla Manager Suspend & Resume feature

6 May 2021, 12:00 am by Ultrabug

On Scylla Manager Suspend & Resume feature

!!! warning "Disclaimer" This blog post is neither a rant nor intended to undermine the great work that...

Renaming and reshaping Scylla tables using scylla-migrator

6 November 2020, 12:00 am by Ultrabug

We have recently faced a problem where some of the first Scylla tables we created on our main production cluster were not in line any more with the evolved s...

Python scylla-driver: how we unleashed the Scylla monster's performance

24 August 2020, 12:00 am by Ultrabug

At Scylla summit 2019 I had the chance to meet Israel Fruchter and we dreamed of working on adding **shard...

Scylla Summit 2019

28 December 2019, 12:00 am by Ultrabug

I've had the pleasure to attend again and present at the Scylla Summit in San Francisco and the honor to be awarded the...

A Small Utility to Help With Extracting Code Snippets

10 August 2019, 12:00 am by Posts on RustyRazorblade Consulting

It’s been a while since I’ve written anything here. Part of the reason has been due to the writing I’ve done over on the blog at The Last Pickle. In the lsat few years, I’ve written about our tlp-stress tool, tips for new Cassandra clusters, and a variety of performance posts related to Compaction, Compression, and GC Tuning.

The other reason is the eight blog posts I’ve got in the draft folder. One of the reasons why there are so many is the way I write. If the post is programming related, I usually start with the post, then start coding, pull snippets out, learn more, rework the post, then rework snippets. It’s an annoying, manual process. The posts sitting in my draft folder have incomplete code, and reworking the code is a tedious process that I get annoyed with, leading to abandoned posts.

Scylla: four ways to optimize your disk space consumption

29 March 2019, 12:00 am by Ultrabug

We recently had to face free disk space outages on some of our scylla clusters and we learnt some very interesting things while outlining some improvements t...