In short, on wide Partitions a key cache miss still results in two additional disk reads, as it did before Cassandra 3.6, but now a key cache hit incurs a disk read to the -Index.db file where it did not before Cassandra 3.6. These IndexInfo objects provide a sampling of positional offsets for Rows within a Partition, creating an index. So, I wanted to put the whole “wide rows” and the performance edge claims that wide-row data model said to offer to the test. Common use-cases include IoT, messaging, and fraud detection. In fact there are several claims by, Architecture and data model for whatsapp– with cassandra, 2. Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Each object specifies the offset the page starts at, the first Row and the last Row. While version 2.2.13 crashed repeatedly during this test, 3.11.3 was able to write over 30 million Rows to a single Partition before Cassandra Out-of-Memory crashed. So the usecase here is contrived just for evaluating the power of cassandra. It illustrates that reads of wide Partitions do not create the heap pressure that writes do. Neither form of IndexInfo objects reside long in the heap and thus the GC Pause Time is barely visible in comparison to Cassandra 2.2.13 despite the additional numbers of IndexInfo objects created via deserialization. The number of garbage collection intervals and the oscillations in heap size are far more frequent. This is why the column_index_cache_size_in_kb setting was added in Cassandra 3.6 and the objects are now serialized as they are created. Below figure shows it. Couple of things about the physical storage, this is important because when you are attempting to get the older messages from the server i.e not through CDC mechanism we need a similar user experience. The above diagrams display an oscillating heap when IndexInfo objects are created, and many garbage collection intervals, yet GC Pause Time remains low, if at all noticeable. This subclass is chosen by the the ColumnIndex and depends on the size of the partition: RowIndexEntry is used when there are no Clusterable objects in the Partition, such as when there is only a static Row. When applied to multiple Rows, this will cause greater churn of additions and evictions of cache entries. But in a column oriented database one row can have columns (a,b,c) and another (a,b) or just (a). If the compression option is not specified, LZ4Compressor will be used, which is known for it’s excellent performance and compression rate. Map--Correct Which OS does Cassandra support? Wide rows defined. The GC Pause Time during the stress testing period is now consistently higher and comparable to the CPU Time. Thus, Cassandra gives you the flexibility normally associated with schemaless systems, while also delivering the benefits of having a defined schema. Any Cassandra node under this much duress from garbage collection is not healthy. For an excellent in-depth examination of this, see Aaron’s blog post on the Cassandra 3.x Storage Engine. In addition to options such as using TWCS for our compaction strategy, specifying gc grace seconds, and caching options, we can also tell Cassandra how we want it to compress our data. Both Cassandra versions were started as single-node clusters with default configurations, excepting heap customization in the cassandra–env.sh: In Cassandra only the configured concurrency of memtable flushes and compactors determines how many Partitions are processed by a node and thus pressuring its heap at any one time. The following command lines then implemented the tlp-stress tool: Each time tlp-stress executed it was immediately followed by a command to ensure the full count of specified Rows passed through the memtable flush and were written to disk: The graphs in the sections below, taken from the Apache NetBeans Profiler, illustrate how the ShallowIndexEntry in Cassandra version 3.11 avoids keeping IndexInfo objects in memory. Since it is a contrived usecase for now lets forget the semantics of how actually whatsapp worked in terms storing the messages and make some assumptions and assertions. Linearly scalable by simply adding more nodes to the cluster. 2. 3. Note there are significantly more instantiated IndexInfo objects, but barely any noticeable GC Pause Time. tl/dr: Apache Cassandra is a NoSQL database with flexible deployment options that’s highly performant (especially for writes), scalable, fault-tolerant, and proven in production. 2. Facebook released Cassandra as an open source project on google code in July 2008. The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Rumour has it, Facebook hired ex-Amazon engineers who wrote Dynamo to build cassandra. We will demonstrate that well designed data models can go beyond the existing 400MB recommendation without nodes crashing through heap pressure. A tlp-stress profile for wide Partitions was written, as no suitable profile existed. This is especially true if you need to make a high volume of requests for a subset of data that exists entirely in one wide row. The second one gives some positive and negative points about it. The following graph reruns the benchmark on Cassandra version 3.11.3 over a longer period of time with a read to write ratio of 10:1. Anyways, a sound starting point is to have a nice use-case to test with. Objects created during subsequent major compaction, Heap profiled during tlp-stress and major compaction. So essentially you can query these CDC tables, for the filter conditions you have mentioned in WHERE clause if records are exist, it will be returned if not, the query blocks until new records arrive. Apache Cassandra is an extremely powerful open-source distributed database system that works really well to handle huge volumes of records spread across multiple commodity servers. Indeed, before the ShallowIndexedEntry was added in Cassandra version 3.6, a single wide Row could fill the key cache, reducing the hit rate efficiency. We can also further group data within a row using super or composite columns as discussed later. I will get to that shortly. Below, we walk through how Cassandra writes Partitions to disk in 3.11, look at how wide Partitions impact read latencies, and then present our testing and verification of wide Partition impacts on the cluster, using the work we did with Backblaze. So I took the real world scenario of messenger platforms and thought what if cassandra is tasked to be the source of truth for the entire platform right from user profiles to actual messages. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data. These results extrapolate to production environments. If you say, delete and re-install the app you can still recover chat messages from the server, unlike whatsapp. Cassandra's storage engine uses composite columns under the hood to store clustered rows. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data. The ShallowIndexEntry subclass serializes IndexInfo objects to disk as they are created and references these objects using only their position in the file. Let’s use Cassandra as a filestore — Cassandra does not like too big rows. Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. 4. The size of the key cache is limited by the key_cache_size_in_mb configuration setting. The third screenshot shows the CPU & GC Pause Time and the heap profile over the time writes started until the compaction was completed. HDFS’s architecture is hierarchical. Apache Cassandra Data Modeling and Query Best Practices ; ... each record will be its own row. In this article. In our second post, we discussed JVM tuning, and how the different JVM settings can have an affect on different workloads.. #1 OSS Committer DataStax has contributed a majority of the open-source Cassandra code commits and we are one of the driving forces behind Apache Cassandra 4.0. Also, the choice of the rowkey serves a purpose, any chat (individual or group) may not hit the 2 billion max messages per day. Wide Partitions, however, can decrease the efficiency of this key cache optimization because fewer hot Partitions will fit into the allocated cache size. you can subscribe to the changes i.e inserts, updates, deletes of a particular table. While the theoretical limit on the number of cells per Partition has always been two billion cells, the reality has been quite different, as the impacts of heap pressure show. With all these information lets look at how the data is physically stored and be sure it is not stored the way you think. In this final test of Cassandra version 2.2.13, the results were difficult to reproduce reliably, as more often than not this test Out-of-Memory crashed from GC heap pressure. It became an Apache incubator project in March 2009. To test the effects of this and the other performance enhancement features introduced in version 3 we compared how Cassandra 2.2.13 and 3.11.3 performed when inserting one hundred thousand, one million, or ten million Rows were each written to a single Partition. Conclusions. On the contrary, Cassandra’s architecture consists of multiple peer-to-peer nodes and resembles a ring. Check out this user story in JIRA for more details. As with the earlier version test of this size, the following two screenshots shows the number of IndexInfo objects instantiated during the write benchmark and during the subsequent compaction process. There are no concepts of foreign keys, referential integrity, or joins in Cassandra (or in most any other NOSQL DATABASE). I am working to use data and decentralization techniques (like BlockChain) to solve big societal problems. Wide row column families are heavily used (with composite columns) to build custom indexes in Cassandra. It contains a master node, as well as numerous slave nodes. The local store of the device is the canonical data store for messages. The relationship betweeen Partition size and the number of objects was quantified by Robert Stupp in his presentation, Myths of Big Partitions: Cassandra’s key cache is an optimization that is enabled by default and helps to improve the speed and efficiency of the read path by reducing the amount of disk activity per read. Each row is referenced by a primary key, also called the row key. For avoiding any confusions and since we are building a variant of whatsapp let me call it Whatsapp– (Whatsapp minus minus) for the rest of this post. Here, the first two screenshots show the number of IndexInfo objects instantiated during the write benchmark and during the subsequent compaction process. We can also further group data within a row using super or composite columns as discussed later. Automatic workload and data balancing. APPLIES TO: Cassandra API Azure Cosmos DB is Microsoft's globally distributed multi-model database service. Data Partitioning- Apache Cassandra is a distributed database system using a shared nothing architecture. When a read operation in the storage engine gets a cache hit it avoids having to access the –Summary.db and –Index.db SSTable components, which reduces that read request’s latency. You probably shouldn’t use Cassandra if you have a small dataset, have highly transactional data, or need to do joins/aggregations without other tools. During this time the heap also starts to increase and decrease more frequently as it fills up and then the Garbage Collector cleans it out. The result was 30M+ rows, which is ~12GB of data on disk. If you take a look at the Readme file at Apache Cassandra git repo, it says that,; Cassandra is a partitioned row store. Cassandra is wide column store, and, as such, essentially a hybrid between a key-value and a tabular database management system. If you want to store big files, then you need to split them into multiple parts. Wide row column families are heavily used (with composite columns) to build custom indexes in Cassandra. Notably, the IndexInfo objects are instantiated far more often, but are referenced for much shorter periods of time. This is especially true if you need to make a high volume of requests for a subset of data that exists entirely in one wide row. This means that all the logical rows with the same partition key get stored as a single physical "wide row." The third screenshot shows the GC Pause Time and the heap profile from the time writes started until compaction was completed. Each row is referenced by a primary key, also called the row key. Introducing the ShallowIndexedEntry into Cassandra version 3.6 creates a measurable improvement in the performance of wide Partitions. In our case a days worth data of a chat can be read with a single disk seek and fetch. I don’t have exact performance metrics with me but it was It was impressive without too much visible lag. Each SSTable contain a set of files, and the (–Data.db) file contains numerous Partitions. DynamoDB is fully managed, serverless, and provides multi-master cross-region replication, encryption at rest, and managed backup and restore. APPLIES TO: Cassandra API Azure Cosmos DB is Microsoft's globally distributed multi-model database service. The partition grew to be ~40MB, the same as with Cassandra 2.2.13. Integration with AWS Identity and Access Management (IAM) enables DynamoDB customers to implement fine-grained access control for their data security needs. At a 10000 foot level Cassa… Just to be clear about the, This is where the real power of wide-rows come in. Both Windows and Linux--Correct Protocol for communicating timeout info among nodes is _____. I am a distributed data systems researcher and a computational linguist. Before we jump into best practices for running Cassandra on AWS, we should mention that we have many customers who decided to use DynamoDB instead of managing their own Cassandra cluster. The post focuses on the column-oriented storage called wide rows. The layout of a Partition in the –Data.db file has three components: a header, followed by zero or one static rows, which is followed by zero or more ordered Clusterable objects. So, in general, the bigger the Partition, the more IndexInfo objects need to be created when writing to disk - and if they are held in memory until the Partition is fully written to disk they can cause memory pressure. However, in version 3 many improvements were made that affected how Cassandra handles wide Partitions. The following three screenshots shows the number of IndexInfo objects instantiated during the write benchmark, during compaction, and a heap profile. Already at this size the Cassandra JVM is GC thrashing and has occasionally Out-of-Memory crashed. Skinny row (1000 rows per set): On the other hand, a wide row can give rise to read hotspots in the cluster. So I took the real world scenario of messenger platforms and thought what if cassandra is tasked to be the source of truth for the entire platform right from user profiles to actual messages. This basically means rows are uniquely identifiable by (or partitioned by in cassandra terms), https://academy.datastax.com/resources/getting-started-killrchat-example-data-model-messaging, https://www.quora.com/How-long-does-WhatsApp-keep-the-delivered-data-like-images-or-etc-in-the-server, http://www.planetcassandra.org/blog/we-shall-have-order/. A query language that looks a lot like SQL.With the list of features above, why don’t we all use Cassandra for all our database needs? As you see the rowkey of the table is the composite key made out of chatid  and chatdate . Getting Started with Cassandra. Because whatsapp doesn’t store any of user messages, the messages just ‘pass-thru’ the server to the devices. There is also a third column mentioned as a part of the “primary key clause” and also in the “clustering order by clause”. Introduction to Apache Cassandra. I work at the interplay of technology, behavioral economics, and psychology. As a side benefit, you can de-normalize a one-to-many relationship as a wide row without data duplication. 1. Apache Cassandra Data Modeling and Query Best Practices ; ... each record will be its own row. However, doing too many multigets on wide rows can increase memory pressure on nodes, and degrade performance. Can be globally distributed. Remember than in a regular rdbms database, like Oracle, each row stores all values, including empty ones. Rumour has it, Facebook hired ex-Amazon engineers who wrote Dynamo  to build cassandra. I used Instacluster, a managed cassandra provider. Whatsapp– stores all the new chats (individual and group), the messages in cassandra. The partition grew to be ~4GB, the same as with Cassandra 2.2.13. Skinny row (1000 rows per set): On the other hand, a wide row can give rise to read hotspots in the cluster. Avinash Lakshman and Prashant Malik initially developed Cassandra at Facebook to power the Facebook inbox search feature. Just to be clear about the chatid , for every new chat i.e when the user hits the new chat icon (highlighted in the below figure) an unique uuid is generated. The test and results are reproduced below. The third screenshot shows the CPU & GC Pause Times and the heap profile from the time writes started through when the compaction was completed. High available by design. CDC mechanism provides this out of the box, so you don’t need additional writes into distributed pub/sub infrastructure like Kafka. When we create a table in Cassandra, we can specify a variety of table options in addition to our fields. In these diagrams the garbage collection intervals are easy to identify and isolate from one another. The above diagrams show a wildly oscillating heap as many IndexInfo objects are created, and shows many garbage collection intervals, yet the GC Pause Time remains low, if at all noticeable. So, I wanted to put the whole “wide rows” and the performance edge claims that wide-row data model said to offer to the test. In order to understand Cassandra's architecture it is important to understand some key concepts, data structures and algorithms frequently used by Cassandra. Apache Cassandra is not the easiest database to learn on the market. I have heard that in reality up to 50.000 cols/50 MB are fine; 50.000-100.000 cols/100 MB are OK but require some tuning; and that one should never go above 100.000/100 MB columns per row. I’d like to have each record belong to one large wide row (per server node) so I can have them sorted or clustered on some other column. Theoretically, Cassandra allows up to 2 billion columns in a wide row. Also, I am sure you would have noticed the flexible data types like lists and maps. However, doing too many multigets on wide rows can increase memory pressure on nodes, and degrade performance. You don’t even have to do a order by in memory as the clustering key has helped to store the data in desc order in the disk. So, I wanted to put the whole “wide rows” and the performance edge claims that wide-row data model said to offer to the test. Each key cache entry is identified by a combination of the keyspace, table name, SSTable, and the Partition key. This is our third post in our series on performance tuning with Apache Cassandra. So there can be server components blocking with queries, if the record arrives you can write it back to the message delivery back bone like GCM or APNs or 3rd party services like AWS SNS. These indices facilitate locating the data on disk within the –Data.db file. It only dissipates when the benchmark performs the flush and compaction. If you take a look at the Readme file at Apache Cassandra git repo, it says that,; Cassandra is a partitioned row store. 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