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.

Powering AI Workloads with Intelligent Data Infrastructure and Open Source

22 July 2024, 2:43 pm by Apache Cassandra - Instaclustr

In the rapidly evolving technological landscape, artificial intelligence (AI) is emerging as a driving force behind innovation and efficiency. However, to harness its full potential, enterprises need suitable data infrastructures that can support AI workloads effectively. 

This blog explores how intelligent data infrastructure, combined with open source technologies, is revolutionizing AI applications across various business functions. It outlines the benefits of leveraging existing infrastructure and highlights key open source databases that are indispensable for powering AI. 

The Power of Open Source in AI Solutions 

Open source technologies have long been celebrated for their flexibility, community support, and cost-efficiency. In the realm of AI these advantages are magnified. Here’s why open source is indispensable for AI-fueled solutions: 

  1. Cost Efficiency: Open source solutions eliminate licensing fees, making them an attractive option for businesses looking to optimize their budgets.
  2. Community Support: A vibrant community of developers constantly improves these platforms, ensuring they remain cutting-edge.
  3. Flexibility and Customization: Open source tools can be tailored to meet specific needs, allowing enterprises to build solutions that align perfectly with their goals. 
  4. Transparency and Security: With open source, you have visibility into the code, which allows for better security audits and trustworthiness. 

Vector Databases: A Key Component for AI Workloads 

Vector databases are increasingly indispensable for AI workloads. They store data in high-dimensional vectors, which AI models use to understand patterns and relationships. This capability is crucial for applications involving natural language processing, image recognition, and recommendation systems. 

Vector databases use embedding vectors (lists of numbers) to represent data similarities and plot relationships spatially. For example, “plant” and “shrub” will have closer vector coordinates than “plant” and “car”. This allows enterprises to build their own LLMs, explore large text datasets, and enhance search capabilities. 

Vector databases and embeddings also support retrieval augmented generation (RAG), which improves LLM accuracy by refining its understanding of new information. For example, RAG can let users query documentation by creating embeddings from an enterprise’s documents, translating words into vectors, finding similar words in the documentation, and retrieving relevant information. This data is then provided to an LLM, enabling it to generate accurate text answers for users. 

The Role of Vector Databases in AI: 

  1. Efficient Data Handling: Vector databases excel at handling large volumes of data efficiently, which is essential for training and deploying AI models. 
  2. High Performance: They offer high-speed retrieval and processing of complex data types, ensuring AI applications run smoothly. 
  3. Scalability: With the ability to scale horizontally, vector databases can grow alongside your AI initiatives without compromising performance. 

Leveraging Existing Infrastructure for AI Workloads 

Contrary to popular belief, it isn’t necessary to invest in new and exotic specialized data layer solutions. Your existing infrastructure can often support AI workloads with a few strategic enhancements: 

  1. Evaluate Current Capabilities: Start by assessing your current data infrastructure to identify any gaps or areas for improvement. 
  2. Upgrade Where Necessary: Consider upgrading components such as storage, network speed, and computing power to meet the demands of AI workloads. 
  3. Integrate with AI Tools: Ensure your infrastructure is compatible with leading AI tools and platforms to facilitate seamless integration. 

Open Source Databases for Enterprise AI 

Several open source databases are particularly well-suited for enterprise AI applications. Let‘s look at the 3 free open source databases that enterprise teams can leverage as they scale their intelligent data infrastructure for storing those embedding vectors: 

PostgreSQL® and pgvector 

“The world’s most advanced open source relational database“, PostgreSQL is also one of the most widely deployed, meaning that most enterprises will already have a strong foothold in the technology. The pgvector extension turns Postgres into a high-performance vector store, offering a path of least resistance for organizations familiar with PostgreSQL to quickly stand-up intelligent data infrastructure. 

From a RAG and LLM training perspective, pgvector excels at enabling distance-based embedding search, exact nearest neighbor search, and approximate nearest neighbor search. pgvector efficiently captures semantic similarities using L2 distance, inner product, and (the OpenAI-recommended) cosine distance. Teams can also harness OpenAI’s embeddings model (available as an API) to calculate embeddings for documentation and user queries. As an enterprise-ready open source option, pgvector is an already-proven solution for achieving efficient, accurate, and performant LLMs, helping equip teams to confidently launch differentiated and AI-fueled applications into production.

OpenSearch® 

Because OpenSearch is a mature search and analytics engine already popular with a wide swath of enterprises, new and current users will be glad to know that the open source solution is ready to up the pace of AI application development as a singular search, analytics, and vector database.  

OpenSearch has long offered low latency, high availability, and the scale to handle tens of billions of vectors while backing stable applications. It provides great nearest-neighbor search functionality to support vector, lexical, and hybrid search and analytics. These capabilities significantly simplify the implementation of AI solutions, from generative AI  agents to recommendation engines with trustworthy results and minimal hallucinations. 

Apache Cassandra® 5.0 with Native Vector Indexing

Known for its linear scalability and fault-tolerance on commodity hardware or cloud infrastructure, Apache Cassandra is a reliable choice for enterprise-grade AI applications. The newest version of the highly popular open source Apache Cassandra database introduces several new features built for AI workloads. It now includes Vector Search and Native Vector indexing capabilities.

Additionally, there is a new vector data type specifically for saving and retrieving embedding vectors, and new CQL functions for easily executing on those capabilities. By adding these features, Apache Cassandra 5.0 has emerged as an especially ideal database for intelligent data strategies and for enterprises rapidly building out AI applications across myriad use cases.

Cassandra’s earned reputation for delivering high availability and scalability now adds AI-specific functionality, making it one of the most enticing open source options for enterprises. 

Open Source Opens the Door to Successful AI Workloads 

Clearly, given the tremendously rapid pace at which AI technology is advancing, enterprises cannot afford to wait to build out differentiated AI applications. But in this pursuit, engaging with the wrong proprietary data-layer solutions—and suffering the pitfalls of vendor lock-in or simply mismatched features—can easily be (and, for some, already is) a fatal setback. Instead, tapping into one of the very capable open source vector databases available will allow enterprises to put themselves in a more advantageous position. 

When leveraging open source databases for AI workloads, consider the following: 

  • Data Security: Ensure robust security measures are in place to protect sensitive data. 
  • Scalability: Plan for future growth by choosing solutions that can scale with your needs. 
  • Resource Allocation: Allocate sufficient resources, such as computing power and storage, to support AI applications. 
  • Governance and Compliance: Adhere to governance and compliance standards to ensure responsible use of AI. 

Conclusion 

Intelligent data infrastructure and open source technologies are revolutionizing the way enterprises approach AI workloads. By leveraging existing infrastructure and integrating powerful open source databases, organizations can unlock the full potential of AI, driving innovation and efficiency. 

Ready to take your AI initiatives to the next level? Leverage a single platform to help you design, deploy and monitor the infrastructure to support the capabilities of PostgreSQL with pgvector, OpenSearch, and Apache Cassandra 5.0 today.

And for more insights and expert guidance, don’t hesitate to contact us and speak with one of our open source experts! 

The post Powering AI Workloads with Intelligent Data Infrastructure and Open Source appeared first on Instaclustr.

How Does Data Modeling Change in Apache Cassandra® 5.0 With Storage-Attached Indexes?

9 July 2024, 2:18 am by Apache Cassandra - Instaclustr

Data modeling in Apache Cassandra® is an important topic—how you model your data can affect your cluster’s performance, costs, etc. Today I’ll be looking at a new feature in Cassandra 5.0 called Storage -Attached Indexes (SAI), and how they affect the way you model data in Cassandra databases. 

First, I’ll briefly cover what SAIs are (for more information about SAIs, check out this post). Then I’ll look at 3 use cases where your modeling strategy could change with SAI. Finally, I’ll talk about benefits and constraints of SAIs. and constraints of SAIs. 

What Are Storage–Attached Indexes? 

From the Cassandra 5.0 Documentation, Storage –Attached Indexes (SAIs) “[provide] an indexing mechanism that is closely integrated with the Cassandra storage engine to make data modeling easier for users.” Secondary Indexing, which is indexing values on properties that are not part of the Primary Key for that table, has been available for Cassandra in the past (called SASI and 2i). However, SAIs will replace the existing functionality, as it will be deprecated in 5.0, and then tentatively removed in Cassandra 6.0. 

This is because SAIs improve upon the older methods in a lot of key ways. For one, according to the devs, SAIs are the fastest indexing method for Cassandra clusters. This performance boost was a plus for using indexing in production environments. It also lowered the data storage overhead over prior implementations, which lowers costs by reducing the need for database storage, which induces operational costs, and by reducing latency when dealing with indexes, lowering a loss of user interaction due to high latency. 

How Do SAIs work? 

SAIs are implemented as part of the SSTables, or Sorted String Tables, of a Cassandra database. This is because SAIs index Memtables and SSTables as they are written. It filters from both in-memory and on-disk sources, filtering them out into a series of indexed columns at read time. I’m not going to go into too much detail here because there are a lot of existing resources on this exciting topic: see the Cassandra 5.0 Documentation and the Instaclustr site for examples. 

The main thing to keep in mind is that SAIs are attached to Cassandra’s storage engine, and it’s much more performant from speed, scalability, and data storage angles as a result. This means that you can use indexing reliably in production beginning with Cassandra 5.0, which allows data modeling to be improved very quickly. 

To learn more about how SAIs work, check out this piece from the Apache Cassandra blog. 

What Is SAI For? 

SAI is a filtering engine, and while it does have some functionality overlap with search engines, it directly says it is “not an enterprise search engine” (source). 

SAI is meant for creating filters on non-primary-key or composite partition keys (source), essentially meaning that you can enable a ‘WHERE’ clause on any column in your Cassandra 5.0 database. This makes queries a lot more flexible without sacrificing latency or storage space as with prior methods.  

How Can We Use SAI When Data Modeling in Cassandra 5.0? 

Because of the increased scalability and performance of SAIs, data modeling in Cassandra 5.0 will most definitely change.  

You will be able to search collections more thoroughly and easily, for instance, indexing is more of an option when designing your Cassandra queries. This will also allow new query types, which can improve your existing queries—which by Cassandra’s design paradigm changes your table design. 

But what if you’re not on a greenfield project and want to use SAIs? No problem! SAI is backwards-compatible, and you can migrate your application one index at a time if you need. 

How Do Storage–Attached Indexes Affect Data Modeling in Apache Cassandra 5.0? 

Cassandra’s SAI was designed with data modeling in mind (source). It unlocks new query patterns that make data modeling easier in quite a few cases. In the Cassandra team’s words: “You can create a table that is most natural for you, write to just that table, and query it any way you want.” (source) 

I think another great way to look at how SAIs affect data modeling is by looking at some queries that could be asked of SAI data. This is because Cassandra data modeling relies heavily on the queries that will be used to retrieve the data. I’ll take a look at 2 use cases: indexing as a means of searching a collection in a row and indexing to manage a one-to-many relationship. 

Use Case: Querying on Values of Non-Primary-Key Columns 

You may find you’re searching for records with a particular value in a particular column often in a table. An example may be a search form for a large email inbox with lots of filters. You could find yourself looking at a record like: 

  • Subject 
  • Sender 
  • Receiver 
  • Body 
  • Time sent 

Your table creation may look like: 

CREATE KEYSPACE IF NOT EXISTS inbox 

WITH REPLICATION = { 

  'class' : 'SimpleStrategy', 

  'replication_factor' : 3 

}; 

CREATE TABLE IF NOT EXISTS emails ( 

  id int,  

  sender text,  

  receivers text,  

  subject text,  

  body text, 

  timeSent timestamp, 

  PRIMARY KEY (id)); 

};

If you allow users to search for a particular subject or sender, and the data set is large, not having SAIs could make query times painful: 

SELECT * FROM emails WHERE emails.sender == “sam.example@example.com”

To fix this problem, we can create secondary indexes on our sender, receiver, and body fields: 

CREATE CUSTOM INDEX sender_sai_idx ON Inbox.emails (sender)  

USING 'StorageAttachedIndex'  

WITH OPTIONS = {'case_sensitive': 'false', 'normalize': 'true', 'ascii': 'true'}; 

CREATE INDEX IF NOT EXISTS receiver_sai_idx on Inbox.emails (receiver) 

USING 'StorageAttachedIndex'  

WITH OPTIONS = {'case_sensitive': 'false', 'normalize': 'true', 'ascii': 'true'}; 

CREATE CUSTOM INDEX body_sai_idx ON Inbox.emails (body)  

USING 'StorageAttachedIndex'  

WITH OPTIONS = {'case_sensitive': 'false', 'normalize': 'true', 'ascii': 'true'}; 

CREATE CUSTOM INDEX subject_sai_idx ON Inbox.emails (subject)  

USING 'StorageAttachedIndex'  

WITH OPTIONS = {'case_sensitive': 'false', 'normalize': 'true', 'ascii': 'true'};

Once you’ve established the indexes, you can run the same query and it will automatically use the SAI index to find all emails with a sender of “sam.example@examplemail.com” OR by subject match/body match.  Note that although the data model changed with the inclusion of the indexes, the SELECT query does not change, and the fields of the table stayed the same as well! 

Use Case: Managing One-To-Many Relationships 

Going back to the previous example, one email could have many recipients. Prior to secondary indexes, you would need to scan every row in the collection of every row in the table in order to query on recipients. This could be solved in a few ways. One is to create a join table for recipients that contains an id, email id, and recipient. This becomes complicated when the constraint that each email should only appear once per email is added. With SAI, we now have an index-based solution—create an index on a collection of recipients for each row. 

The script to create the table and indices changes a bit: 

id int,  

  sender text,  

  receivers set<text>,  

  subject text,  

  body text, 

  timeSent timestamp, 

  PRIMARY KEY (id)); 

};

The text type of receivers changes to a set<text>. A set is used because each email should only occur once. This takes the logic you would have had to implement for the join table solution and moves it to Cassandra.  

The indexing code remains mostly the same, except for the creation of the index for receivers: 

CREATE INDEX IF NOT EXISTS receivers_sai_idx on Inbox.emails (receivers)

That’s it! One line of CQL and there’s now an index on receivers. We can query for emails with a particular receiver: 

SELECT * FROM emails WHERE emails.receievers CONTAINS “sam.example@examplemail.com”

There are many one-to-many relationships that can be simplified in Cassandra with the use of secondary indexes and SAI. 

What Are the Benefits of Data Modeling With Storage Attached Indexes? 

There are many benefits to using SAI when data modeling in Cassandra 5.0: 

  • Query performance: because of SAI’s implementation, it has much faster query speeds than previous implementations, and indexed data is generally faster to search than unindexed data. This means you have more flexibility to search within your data and write queries that search non-primary-key columns and collections. 
  • Move over piecemeal: SAI’s backwards compatibility, coupled with how little your table structure has to change to add SAIs, means you can move over your data models piece by piece, meaning moving is easier.  
  • Data storage overhead: SAI has much lower data overhead than previous secondary index implementations, meaning more flexibility in what you can store in your data models without impacting overall storage needs. 
  • More complex queries/features: SAI allows you to write much more thorough queries when looking through SAIs, and offers up a lot of new functionality, like: 
    • Vector Search 
    • Numeric Range queries 
    • AND queries within indexes 
    • Support for map/set/ 

What Are the Constraints of Storage–Attached Indexes? 

While there are benefits to SAI, there are also a few constraints, including: 

  • Because SAI is attached to the SSTable mechanism, the performance of queries on indexed columns will be “highly dependent on the compaction strategy in use” (per the Cassandra 5.0 CEP-7) 
  • SAI is not designed for unlimited-size data sets, such as logs; indexing on a dataset like that would cause performance issues. The reason for this is read latency at higher row counts spread across a cluster. It is also related to consistency level (CL), as the higher the CL is, the more nodes you’ll have to ping on larger datasets. (Source). 
  • Query complexity: while you can query as many indexes as you like, when you do so, you incur a cost related to the number of index values processed. Be aware when designing queries to select from as few indexes as necessary. 
  • You cannot index multiple columns in one index, as there is a 1-to-1 mapping of an SAI index to a column. You can however create separate indexes and query them simultaneously. 

This is a v1, and some features, like the LIKE comparison for strings, the OR operator, and global sorting are all slated for v2. 

Disk usage: SAI uses an extra 20-35% disk space over unindexed data; note that over previous versions of indexing, it consumes much less (source). You shouldn’t just make every column an index if you don’t need to, saving disk space and maintaining query performance. 

Conclusion 

SAI is a very robust solution for secondary indexes, and their addition to Cassandra 5.0 opens the door for several new data modelling strategies—from searching non-primary-key columns, to managing one-to-many relationships, to vector search. To learn more about SAI, read this post from the Instaclustr by NetApp blog, or check out the documentation for Cassandra 5.0. 

If you’d like to test SAI without setting up and configuring Cassandra yourself, Instaclustr has a free trial and you can spin up Cassandra 5.0 clusters today through a public preview! Instaclustr also offers a bunch of educational content about Cassandra 5.0. 

 

The post How Does Data Modeling Change in Apache Cassandra® 5.0 With Storage-Attached Indexes? appeared first on Instaclustr.

Cassandra Lucene Index: Update

2 July 2024, 5:07 am by Apache Cassandra - Instaclustr

**An important update regarding support of Cassandra Lucene Index for Apache Cassandra® 5.0 and the retirement of Apache Lucene Add-On on the Instaclustr Managed Platform.** 

Instaclustr by NetApp has been maintaining the new fork of the Cassandra Lucene Index plug-in since its announcement in 2018. After extensive evaluation, we have decided not to upgrade the Cassandra Lucene Index to support Apache Cassandra® 5.0. This decision aligns with the evolving needs of the Cassandra community and the capabilities offered by the Storage–Attached Indexing (SAI) in Cassandra 5.0.  

SAI introduces significant improvements in secondary indexing, while simplifying data modeling and creating new use cases in Cassandra, such as Vector Search. While SAI is not a direct replacement for the Cassandra Lucene Index, it offers a more efficient alternative for many indexing needs.  

For applications requiring advanced indexing features, such as full-text search or geospatial queries, users can consider external integrations, such as OpenSearch®, that offer numerous full-text search and advanced analysis features. 

We are committed to maintaining the Cassandra Lucene Index for currently supported and newer versions of Apache Cassandra 4 (including minor and patch-level versions) for users who rely on its advanced search capabilities. We will continue to release bug fixes and provide necessary security patches for the supported versions in the public repository. 

Retiring Apache Lucene™ Add-On for Instaclustr for Apache Cassandra 

Similarly, Instaclustr is commencing the retirement process of the Apache Lucene add-on on its Instaclustr Managed Platform. The offering will move to the Closed state on July 31, 2024. This means that the add-on will no longer be available for new customers.  

However, it will continue to be fully supported for existing customers with no restrictions on SLAs, and new deployments will be permitted by exception. Existing customers should be aware that the add-on will not be supported for Cassandra 5.0. For more details about our lifecycle policies, please visit our website here.  

Instaclustr will work with the existing customers to ensure a smooth transition during this period. Support and documentation will remain in place for our customers running the Lucene add–on on their clusters.  

For those transitioning to, or already using the Cassandra 5.0 beta version, we recommend exploring how Storage-Attached Indexing can help you with your indexing needs. You can try the SAI feature as part of the free trial on the Instaclustr Managed Platform.  

We thank you for your understanding and support as we continue to adapt and respond to the community’s needs. 

If you have any questions about this announcement, please contact us at support@instaclustr.com. 

The post Cassandra Lucene Index: Update appeared first on Instaclustr.

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22 September 2015, 11:00 am by Alteroot:~# - cassandra

Cassandra usually output all its logs in a system.log file. It uses log4j old 1.2 version for cassandra 2.0, and since 2.1, logback, which of course use different syntax :)
Logs can be enhanced with some configuration. These explanations works with Cassandra 2.0.x and Cassandra 2.1.x, I haven’t tested others versions yet.

I wanted to split logs in different files, depending on their “sources” (repair, compaction, tombstones etc), to ease debugging, while keeping the system.log as usual.

For example, to declare 2 new files to handle, say Repair and Tombstones logs :

Cassandra 2.0 :

You need to declare each new log files in log4j-server.properties file.

[...]
## Repair
log4j.appender.Repair=org.apache.log4j.RollingFileAppender
log4j.appender.Repair.maxFileSize=20MB
log4j.appender.Repair.maxBackupIndex=50
log4j.appender.Repair.layout=org.apache.log4j.PatternLayout
log4j.appender.Repair.layout.ConversionPattern=%5p [%t] %d{ISO8601} %F (line %L) %m%n
## Edit the next line to point to your logs directory
log4j.appender.Repair.File=/var/log/cassandra/repair.log

## Tombstones
log4j.appender.Tombstones=org.apache.log4j.RollingFileAppender
log4j.appender.Tombstones.maxFileSize=20MB
log4j.appender.Tombstones.maxBackupIndex=50
log4j.appender.Tombstones.layout=org.apache.log4j.PatternLayout
log4j.appender.Tombstones.layout.ConversionPattern=%5p [%t] %d{ISO8601} %F (line %L) %m%n
### Edit the next line to point to your logs directory
log4j.appender.Tombstones.File=/home/log/cassandra/tombstones.log

Cassandra 2.1 :

It is in the logback.xml file.

  <appender name="Repair" class="ch.qos.logback.core.rolling.RollingFileAppender">
    <file>${cassandra.logdir}/repair.log</file>
    <rollingPolicy class="ch.qos.logback.core.rolling.FixedWindowRollingPolicy">
      <fileNamePattern>${cassandra.logdir}/system.log.%i.zip</fileNamePattern>
      <minIndex>1</minIndex>
      <maxIndex>20</maxIndex>
    </rollingPolicy>

    <triggeringPolicy class="ch.qos.logback.core.rolling.SizeBasedTriggeringPolicy">
      <maxFileSize>20MB</maxFileSize>
    </triggeringPolicy>
    <encoder>
      <pattern>%-5level [%thread] %date{ISO8601} %F:%L - %msg%n</pattern>
      <!-- old-style log format
      <pattern>%5level [%thread] %date{ISO8601} %F (line %L) %msg%n</pattern>
      -->
    </encoder>
  </appender>

  <appender name="Tombstones" class="ch.qos.logback.core.rolling.RollingFileAppender">
    <file>${cassandra.logdir}/tombstones.log</file>
    <rollingPolicy class="ch.qos.logback.core.rolling.FixedWindowRollingPolicy">
      <fileNamePattern>${cassandra.logdir}/tombstones.log.%i.zip</fileNamePattern>
      <minIndex>1</minIndex>
      <maxIndex>20</maxIndex>
    </rollingPolicy>

    <triggeringPolicy class="ch.qos.logback.core.rolling.SizeBasedTriggeringPolicy">
      <maxFileSize>20MB</maxFileSize>
    </triggeringPolicy>
    <encoder>
      <pattern>%-5level [%thread] %date{ISO8601} %F:%L - %msg%n</pattern>
      <!-- old-style log format
      <pattern>%5level [%thread] %date{ISO8601} %F (line %L) %msg%n</pattern>
      -->
    </encoder>
  </appender>

Now that theses new files are declared, we need to fill them with logs. To do that, simply redirect some Java class to the good file. To redirect the class org.apache.cassandra.db.filter.SliceQueryFilter, loglevel WARN to the Tombstone file, simply add :

Cassandra 2.0 :

log4j.logger.org.apache.cassandra.db.filter.SliceQueryFilter=WARN,Tombstones

Cassandra 2.1 :

<logger name="org.apache.cassandra.db.filter.SliceQueryFilter" level="WARN">
    <appender-ref ref="Tombstones"/>
</logger>

It’s a on-the-fly configuration, so no need to restart Cassandra !
Now you will have dedicated files for each kind of logs.

A list of interesting Cassandra classes :

org.apache.cassandra.service.StorageService, WARN : Repair
org.apache.cassandra.net.OutboundTcpConnection, DEBUG : Repair (haha, theses fucking stuck repair)
org.apache.cassandra.repair, INFO : Repair
org.apache.cassandra.db.HintedHandOffManager, DEBUG : Repair
org.apache.cassandra.streaming.StreamResultFuture, DEBUG : Repair 
org.apache.cassandra.cql3.statements.BatchStatement, WARN : Statements
org.apache.cassandra.db.filter.SliceQueryFilter, WARN : Tombstones

You can find from which java class a log message come from by adding “%c” in log4j/logback “ConversionPattern” :

org.apache.cassandra.db.ColumnFamilyStore INFO  [BatchlogTasks:1] 2015-09-18 16:43:48,261 ColumnFamilyStore.java:939 - Enqueuing flush of batchlog: 226172 (0%) on-heap, 0 (0%) off-heap
org.apache.cassandra.db.Memtable INFO  [MemtableFlushWriter:4213] 2015-09-18 16:43:48,262 Memtable.java:347 - Writing Memtable-batchlog@1145616338(195.566KiB serialized bytes, 205 ops, 0%/0% of on/off-heap limit)
org.apache.cassandra.db.Memtable INFO  [MemtableFlushWriter:4213] 2015-09-18 16:43:48,264 Memtable.java:393 - Completed flushing /home/cassandra/data/system/batchlog/system-batchlog-tmp-ka-4267-Data.db; nothing needed to be retained.  Commitlog position was ReplayPosition(segmentId=1442331704273, position=17281204)

You can disable “additivity” (i.e avoid adding messages in system.log for example) in log4j for a specific class by adding :

log4j.additivity.org.apache.cassandra.db.filter.SliceQueryFilter=false

For logback, you can add additivity=”false” to <logger .../> elements.

To migrate from log4j logs to logback.xml, you can look at http://logback.qos.ch/translator/

Sources :

Note: you can add http://blog.alteroot.org/feed.cassandra.xml to your rss aggregator to follow all my Cassandra posts :)

How to change Cassandra compaction strategy on a production cluster

20 April 2015, 10:00 am by Alteroot:~# - cassandra

I’ll talk about changing Cassandra CompactionStrategy on a live production Cluster.
First of all, an extract of the Cassandra documentation :

Periodic compaction is essential to a healthy Cassandra database because Cassandra does not insert/update in place. As inserts/updates occur, instead of overwriting the rows, Cassandra writes a new timestamped version of the inserted or updated data in another SSTable. Cassandra manages the accumulation of SSTables on disk using compaction. Cassandra also does not delete in place because the SSTable is immutable. Instead, Cassandra marks data to be deleted using a tombstone.

By default, Cassandra use SizeTieredCompactionStrategyi (STC). This strategy triggers a minor compaction when there are a number of similar sized SSTables on disk as configured by the table subproperty, 4 by default.

Another compaction strategy available since Cassandra 1.0 is LeveledCompactionStrategy (LCS) based on LevelDB.
Since 2.0.11, DateTieredCompactionStrategy is also available.

Depending on your needs, you may need to change the compaction strategy on a running cluster. Change this setting involves rewrite ALL sstables to the new strategy, which may take long time and can be cpu / i/o intensive.

I needed to change the compaction strategy on my production cluster to LeveledCompactionStrategy because of our workflow : lot of updates and deletes, wide rows etc.
Moreover, with the default STC, progressively the largest SSTable that is created will not be compacted until the amount of actual data increases four-fold. So it can take long time before old data are really deleted !

Note: You can test a new compactionStrategy on one new node with the write_survey bootstrap option. See the datastax blogpost about it.

The basic procedure to change the CompactionStrategy is to alter the table via cql :

cqlsh> ALTER TABLE mykeyspace.mytable  WITH compaction = { 'class' :  'LeveledCompactionStrategy'  };

If you run alter table to change to LCS like that, all nodes will recompact data at the same time, so performances problems can occurs for hours/days…

A better solution is to migrate nodes by nodes !

You need to change the compaction locally on-the-fly, via the JMX, like in write_survey mode.
I use jmxterm for that. I think I’ll write articles about all theses jmx things :)
For example, to change to LCS on mytable table with jmxterm :

~ java -jar jmxterm-1.0-alpha-4-uber.jar --url instance1:7199                                                      
Welcome to JMX terminal. Type "help" for available commands.
$>domain org.apache.cassandra.db
#domain is set to org.apache.cassandra.db
$>bean org.apache.cassandra.db:columnfamily=mytable,keyspace=mykeyspace,type=ColumnFamilies
#bean is set to org.apache.cassandra.db:columnfamily=mytable,keyspace=mykeyspace,type=ColumnFamilies
$>get CompactionStrategyClass
#mbean = org.apache.cassandra.db:columnfamily=mytable,keyspace=mykeyspace,type=ColumnFamilies:
CompactionStrategyClass = org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy;
$>set CompactionStrategyClass "org.apache.cassandra.db.compaction.LeveledCompactionStrategy" 
#Value of attribute CompactionStrategyClass is set to "org.apache.cassandra.db.compaction.LeveledCompactionStrategy"

A nice one-liner :

~ echo "set -b org.apache.cassandra.db:columnfamily=mytable,keyspace=mykeyspace,type=ColumnFamilies CompactionStrategyClass org.apache.cassandra.db.compaction.LeveledCompactionStrategy" | java -jar jmxterm-1.0-alpha-4-uber.jar --url instance1:7199

On next commitlog flush, the node will start it compaction to rewrite all it mytable sstables to the new strategy.

You can see the progression with nodetool :

~ nodetool compactionstats
pending tasks: 48
compaction type        keyspace           table       completed           total      unit  progress
Compaction        mykeyspace       mytable      4204151584     25676012644     bytes    16.37%
Active compaction remaining time :   0h23m30s

You need to wait for the node to recompact all it sstables, then change the strategy to instance2, etc.
The transition will be done in multiple compactions if you have lots of data. By default new sstables will be 160MB large.

you can monitor you table with nodetool cfstats too :

~ nodetool cfstats mykeyspace.mytable
[...]
Pending Tasks: 0
        Table: sort
        SSTable count: 31
        SSTables in each level: [31/4, 0, 0, 0, 0, 0, 0, 0, 0]
[...]

You can see the 31/4 : it means that there is 31 sstables in L0, whereas cassandra try to have only 4 in L0.

Taken from the code ( src/java/org/apache/cassandra/db/compaction/LeveledManifest.java )

[...]
// L0: 988 [ideal: 4]
// L1: 117 [ideal: 10]
// L2: 12  [ideal: 100]
[...]

When all nodes have the new strategy, let’s go for the global alter table. /!\ If a node restart before the final alter table, it will recompact to default strategy (SizeTiered)!

~ cqlsh 
cqlsh> ALTER TABLE mykeyspace.mytable  WITH compaction = { 'class' :  'LeveledCompactionStrategy'  };

Et voilà, I hope this article will help you :)

My latest Cassandra blogpost was one year ago… I have several in mind (jmx things !) so stay tuned !

Replace a dead node in Cassandra

11 March 2014, 11:00 pm by Alteroot:~# - cassandra

Note (June 2020): this article is old and not really revelant anymore. If you use a modern version of cassandra, look at -Dcassandra.replace_address_first_boot option !

I want to share some tips about my experimentations with Cassandra (version 2.0.x).

I found some documentations on datastax website about replacing a dead node, but it is not suitable for our needs, because in case of hardware crash, we will set up a new node with exactly the same IP (replace “in place”). Update : the documentation in now up to date on datastax !

If you try to start the new node with the same IP, cassandra doesn’t start with :

java.lang.RuntimeException: A node with address /10.20.10.2 already exists, cancelling join. Use cassandra.replace_address if you want to replace this node.

So, we need to use the “cassandra.replace_address” directive (which is not really documented ? :() See this commit and this bug report, available since 1.2.11/2.0.0, it’s an easier solution and it works.

+    - New replace_address to supplant the (now removed) replace_token and
+      replace_node workflows to replace a dead node in place.  Works like the
+      old options, but takes the IP address of the node to be replaced.

It’s a JVM directive, so we can add it at the end of /etc/cassandra/cassandra-env.sh (debian package), for example:

JVM_OPTS="$JVM_OPTS -Dcassandra.replace_address=10.20.10.2"

Of course, 10.20.10.2 = ip of your dead/new node.

Now, start cassandra, and in logs you will see :

INFO [main] 2014-03-10 14:58:17,804 StorageService.java (line 941) JOINING: schema complete, ready to bootstrap
INFO [main] 2014-03-10 14:58:17,805 StorageService.java (line 941) JOINING: waiting for pending range calculation
INFO [main] 2014-03-10 14:58:17,805 StorageService.java (line 941) JOINING: calculation complete, ready to bootstrap
INFO [main] 2014-03-10 14:58:17,805 StorageService.java (line 941) JOINING: Replacing a node with token(s): [...]
[...]
INFO [main] 2014-03-10 14:58:17,844 StorageService.java (line 941) JOINING: Starting to bootstrap...
INFO [main] 2014-03-10 14:58:18,551 StreamResultFuture.java (line 82) [Stream #effef960-6efe-11e3-9a75-3f94ec5476e9] Executing streaming plan for Bootstrap

Node is in boostraping mode and will retrieve data from cluster. This may take lots of time.
If the node is a seed node, a warning will indicate that the node did not auto bootstrap. This is normal, you need to run a nodetool repair on the node.

On the new node :

# nodetools netstats

Mode: JOINING
Bootstrap effef960-6efe-11e3-9a75-3f94ec5476e9
    /10.20.10.1
        Receiving 102 files, 17467071157 bytes total
[...]

After some time, you will see some informations on logs !
On the new node :

 INFO [STREAM-IN-/10.20.10.1] 2014-03-10 15:15:40,363 StreamResultFuture.java (line 215) [Stream #effef960-6efe-11e3-9a75-3f94ec5476e9] All sessions completed
 INFO [main] 2014-03-10 15:15:40,366 StorageService.java (line 970) Bootstrap completed! for the tokens [...]
[...]
 INFO [main] 2014-03-10 15:15:40,412 StorageService.java (line 1371) Node /10.20.10.2 state jump to normal
 WARN [main] 2014-03-10 15:15:40,413 StorageService.java (line 1378) Not updating token metadata for /10.20.30.51 because I am replacing it
 INFO [main] 2014-03-10 15:15:40,419 StorageService.java (line 821) Startup completed! Now serving reads.

And on other nodes :

 INFO [GossipStage:1] 2014-03-10 15:15:40,625 StorageService.java (line 1371) Node /10.20.10.2 state jump to normal

Et voilà, dead node has been replaced !
Don’t forget to REMOVE modifications on cassandra-env.sh after the complete bootstrap !

Enjoy !