The document discusses design patterns for distributed non-relational databases, including consistent hashing for key placement, eventual consistency models, vector clocks for determining history, log-structured merge trees for storage layout, and gossip protocols for cluster management without a single point of failure. It raises questions to ask presenters about scalability, reliability, performance, consistency models, cluster management, data models, and real-life considerations for using such systems.
This document discusses different types of distributed databases. It covers data models like relational, aggregate-oriented, key-value, and document models. It also discusses different distribution models like sharding and replication. Consistency models for distributed databases are explained including eventual consistency and the CAP theorem. Key-value stores are described in more detail as a simple but widely used data model with features like consistency, scaling, and suitable use cases. Specific key-value databases like Redis, Riak, and DynamoDB are mentioned.
Large Scale Lakehouse Implementation Using Structured StreamingDatabricks
Business leads, executives, analysts, and data scientists rely on up-to-date information to make business decision, adjust to the market, meet needs of their customers or run effective supply chain operations.
Come hear how Asurion used Delta, Structured Streaming, AutoLoader and SQL Analytics to improve production data latency from day-minus-one to near real time Asurion’s technical team will share battle tested tips and tricks you only get with certain scale. Asurion data lake executes 4000+ streaming jobs and hosts over 4000 tables in production Data Lake on AWS.
The document summarizes a meetup about Cassandra internals. It provides an agenda that discusses what Cassandra is, its data placement and replication, read and write paths, compaction, and repair. Key concepts covered include Cassandra being decentralized with no single point of failure, its peer-to-peer architecture, and data being eventually consistent. A demo is also included to illustrate gossip, replication, and how data is handled during node failures and recoveries.
This document describes Bigtable, Google's distributed storage system for managing structured data at large scale. Bigtable stores data in sparse, distributed, sorted maps indexed by row key, column key, and timestamp. It is scalable, self-managing, and used by over 60 Google products and services. Bigtable provides high availability and performance through its use of distributed systems techniques like replication, load balancing, and data locality.
Kafka is an open-source distributed commit log service that provides high-throughput messaging functionality. It is designed to handle large volumes of data and different use cases like online and offline processing more efficiently than alternatives like RabbitMQ. Kafka works by partitioning topics into segments spread across clusters of machines, and replicates across these partitions for fault tolerance. It can be used as a central data hub or pipeline for collecting, transforming, and streaming data between systems and applications.
MyRocks is an open source LSM based MySQL database, created by Facebook. This slides introduce MyRocks overview and how we deployed at Facebook, as of 2017.
Redis is an open source, in-memory data structure store that can be used as a database, cache, or message broker. It supports data structures like strings, hashes, lists, sets, sorted sets with ranges and pagination. Redis provides high performance due to its in-memory storage and support for different persistence options like snapshots and append-only files. It uses client/server architecture and supports master-slave replication, partitioning, and failover. Redis is useful for caching, queues, and other transient or non-critical data.
Storm is a distributed and fault-tolerant realtime computation system. It was created at BackType/Twitter to analyze tweets, links, and users on Twitter in realtime. Storm provides scalability, reliability, and ease of programming. It uses components like Zookeeper, ØMQ, and Thrift. A Storm topology defines the flow of data between spouts that read data and bolts that process data. Storm guarantees processing of all data through its reliability APIs and guarantees no data loss even during failures.
Introduction to memcached, a caching service designed for optimizing performance and scaling in the web stack, seen from perspective of MySQL/PHP users. Given for 2nd year students of professional bachelor in ICT at Kaho St. Lieven, Gent.
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013mumrah
Apache Kafka is a distributed publish-subscribe messaging system that allows both publishing and subscribing to streams of records. It uses a distributed commit log that provides low latency and high throughput for handling real-time data feeds. Key features include persistence, replication, partitioning, and clustering.
Kafka Tutorial - Introduction to Apache Kafka (Part 1)Jean-Paul Azar
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We will show the advantages of having a geo-distributed database cluster and how to create one using Galera Cluster for MySQL. We will also discuss the configuration and status variables that are involved and how to deal with typical situations on the WAN such as slow, untrusted or unreliable links, latency and packet loss. We will demonstrate a multi-region cluster on Amazon EC2 and perform some throughput and latency measurements in real-time (video http://galeracluster.com/videos/using-galera-replication-to-create-geo-distributed-clusters-on-the-wan-webinar-video-3/)
A column-oriented database stores data tables as columns rather than rows. This improves the speed of queries that aggregate data over large numbers of records by only reading the necessary columns from disk. Column databases compress data well and avoid reading unnecessary columns. However, they have slower insert speeds and incremental loads compared to row-oriented databases, which store each row together and are faster for queries needing entire rows.
The document discusses intra-cluster replication in Apache Kafka, including its architecture where partitions are replicated across brokers for high availability. Kafka uses a leader and in-sync replicas approach to strongly consistent replication while tolerating failures. Performance considerations in Kafka replication include latency and durability tradeoffs for producers and optimizing throughput for consumers.
Exactly-Once Financial Data Processing at Scale with Flink and PinotFlink Forward
Flink Forward San Francisco 2022.
At Stripe we have created a complete end to end exactly-once processing pipeline to process financial data at scale, by combining the exactly-once power from Flink, Kafka, and Pinot together. The pipeline provides exactly-once guarantee, end-to-end latency within a minute, deduplication against hundreds of billions of keys, and sub-second query latency against the whole dataset with trillion level rows. In this session we will discuss the technical challenges of designing, optimizing, and operating the whole pipeline, including Flink, Kafka, and Pinot. We will also share our lessons learned and the benefits gained from exactly-once processing.
by
Xiang Zhang & Pratyush Sharma & Xiaoman Dong
Apache Kafka is an open-source stream-processing software platform that is used as a messaging queue. It runs as a cluster of servers that can store streams of records in categories called topics. Producers write data to topics and consumers read from topics. The records in topics are organized into partitions which allow for parallelism and scalability. Kafka supports very high throughput, is elastically scalable, has low operational overhead and aims to provide high availability.
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
Big data real time architectures -
How do to big data processing in real time?
What architectures are out there to support this paradigm?
Which one should we choose?
What Advantages / Pitfalls they contain.
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Server-side web development using Erlang and the Web framework provides:
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This document provides an overview of Apache ActiveMQ, an open source messaging platform. It discusses key ActiveMQ concepts like topics, queues, and messaging protocols. It also covers ActiveMQ enterprise features such as high availability, clustering, security, and monitoring. The document concludes by discussing ActiveMQ performance tuning, scaling, and future plans.
This document compares relational and non-relational databases. It discusses how in 2003 the main databases were relational, but by 2010 non-relational databases grew popular in the "NoSQL movement". However, the document argues that there are no truly new database designs and that relational and non-relational databases can be combined. It advises to choose a database based on the specific problem and features needed rather than general classifications. The document provides examples of which types of databases fit certain data and access needs.
VoltDB and Erlang: two very promising beasts, made for the new parallel world, but still lingering in the wings. Not only are they addressing todays challenges but they are using parallel architectures as corner stone of their new and surprising approach to be faster and more productive. What are they good for? Why are we working to team them up?
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This talk presents the case for Erlang as server language, where it shines, how it looks, and how to get started. It details Erlang's secret sauce: microprocesses, actors, atoms, immutable variables, message passing and pattern matching. (Note: for a longer version of this treatment of Erlang only see: Why Erlang? http://www.slideshare.net/eonblast/why-erlang-gdc-online-2012)
VoltDB's inner workings are explained to understand why it can be so incredibly fast and still better than its NoSQL competitors. The well publicized Node.js benchmark clocking in at 695,000 transactions per second is described and the simple steps to get VoltDB up and running to see the prodigy from up close.
Source examples are presented that show Erlang and VoltDB in action.
The speaker is creator and maintainer of the Erlang VoltDB driver Erlvolt.
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About the Speakers
Wei Deng Solutions Architect, DataStax
Solutions Architect for DataStax. I have a strong interest in big data, cloud application and distributed computing practices.
Design Patterns For Distributed NO-reational databaseslovingprince58
This document provides an overview of design patterns for distributed non-relational databases, including:
1) Consistent hashing for partitioning data across nodes, consistency models like eventual consistency, data models like key-value pairs and column families, and storage layouts like log-structured merge trees.
2) Cluster management patterns like the omniscient master and gossip protocols to distribute cluster state information.
3) The document discusses these patterns through examples and diagrams to illustrate how they work.
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This document provides an overview of the Cassandra NoSQL database. It begins with definitions of Cassandra and discusses its history and origins from projects like Bigtable and Dynamo. The document outlines Cassandra's architecture including its peer-to-peer distributed design, data partitioning, replication, and use of gossip protocols for cluster management. It provides examples of key features like tunable consistency levels and flexible schema design. Finally, it discusses companies that use Cassandra like Facebook and provides performance comparisons with MySQL.
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2) Horizontal scaling (scaling out) using distributed systems across multiple commodity servers is more scalable than vertical scaling (scaling up) using more powerful single servers.
3) The CAP theorem states that a distributed system cannot achieve consistency, availability, and partition tolerance simultaneously. NoSQL databases typically choose availability and partition tolerance over strong consistency.
The document summarizes the history and evolution of non-relational databases, known as NoSQL databases. It discusses early database systems like MUMPS and IMS, the development of the relational model in the 1970s, and more recent NoSQL databases developed by companies like Google, Amazon, Facebook to handle large, dynamic datasets across many servers. Pioneering systems like Google's Bigtable and Amazon's Dynamo used techniques like distributed indexing, versioning, and eventual consistency that influenced many open-source NoSQL databases today.
Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above.
Cassandra & Python - Springfield MO User GroupAdam Hutson
Adam Hutson gave an overview of Cassandra and how to use it with Python. Key points include:
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- Best practices include reusing a single session object, specifying keyspaces, authorizing connections, and shutting down clusters to avoid resource leaks.
Distributed Systems: scalability and high availabilityRenato Lucindo
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Cassandra is a distributed database management system designed to handle large amounts of data across many commodity servers. It provides high availability with no single point of failure, linear scalability and performance, and tunable consistency. Some key features include using a dynamic column-based data model, eventual consistency, decentralized control, and supporting replication across multiple data centers. Potential downsides include an ugly Thrift interface, lack of streaming, and tradeoffs between disk and CPU usage.
The document provides an overview of data engineering concepts for data scientists. It discusses the CAP theorem, which states that a distributed system cannot simultaneously provide consistency, availability, and partition tolerance. It describes various data store types and architectures that provide different balances of these properties, such as leader-follower systems that prioritize availability and consistency over partition tolerance. The document also summarizes reference architectures like Lambda and Kappa and discusses the concept of a data lake.
MySQL 5.7 clustering: The developer perspectiveUlf Wendel
(Compiled from revised slides of previous presentations - skip if you know the old presentations)
A summary on clustering MySQL 5.7 with focus on the PHP clients view and the PHP driver. Which kinds on MySQL clusters are there, what are their goal, how does wich one scale, what extra work does which clustering technique put at the client and finally, how the PHP driver (PECL/mysqlnd_ms) helps you.
The document summarizes research on Spinnaker, a scalable and highly available datastore that uses Paxos consensus for replication without relying on a distributed file system. Key points are that Spinnaker achieves timeline consistency, has write performance similar to Cassandra but faster reads, and recovers more quickly from failures than HBase through its replication protocol of shipping log records between nodes rather than using a distributed log.
The document discusses distributed algorithms and techniques used in NoSQL databases related to data consistency, data placement, and system coordination. It covers topics such as replication, failure detection, data partitioning, leader election, and consistency models. Specifically, it analyzes various techniques for data replication that provide different tradeoffs between consistency, availability, scalability, and latency such as anti-entropy protocols, master-slave replication, quorum-based replication, and using vector clocks to detect concurrent writes.
Everything you always wanted to know about Distributed databases, at devoxx l...javier ramirez
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Handling Data in Mega Scale Web SystemsVineet Gupta
The document discusses several challenges faced by large-scale web companies in managing enormous and rapidly growing amounts of data. It provides examples of architectures developed by companies like Google, Amazon, Facebook and others to distribute data and queries across thousands of servers. Key approaches discussed include distributed databases, data partitioning, replication, and eventual consistency.
The document discusses NoSQL databases and Cassandra. It provides background on the rise of NoSQL with the need for large web companies to handle big data in a distributed manner. It introduces the CAP theorem and explains that NoSQL databases sacrifice consistency to achieve availability and partition tolerance. Eventual consistency is described where updates eventually propagate throughout the system. Cassandra is summarized as an open source, distributed, column-oriented database developed at Facebook to be highly scalable and fault tolerant. It uses an eventual consistency model and is robust to failures.
Highly available distributed databases, how they work, javier ramirez at teowakijavier ramirez
This document summarizes key aspects of distributed databases. It discusses master-slave and multi-master replication approaches. It then covers the challenges of achieving availability, partition tolerance and consistency as defined by Brewer's CAP theorem. The rest of the document dives deeper into data distribution, replication, conflict resolution, membership protocols, and allowing the system to operate during network partitions or node failures. It provides examples of gossip protocols, vector clocks, hinted handoff, and anti-entropy processes used in distributed databases. Finally, it notes that building these systems requires clients to be aware of some internal workings and allows for extra credit in building your own distributed database.
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Design Patterns for Distributed Non-Relational Databases
1. Design Patterns for Distributed
Non-Relational Databases
aka
Just Enough Distributed Systems To Be
Dangerous
(in 40 minutes)
Todd Lipcon
(@tlipcon)
Cloudera
June 11, 2009
2. Introduction
Common Underlying Assumptions
Design Patterns
Consistent Hashing
Consistency Models
Data Models
Storage Layouts
Log-Structured Merge Trees
Cluster Management
Omniscient Master
Gossip
Questions to Ask Presenters
3. Why We’re All Here
Scaling up doesn’t work
Scaling out with traditional RDBMSs isn’t so
hot either
Sharding scales, but you lose all the features that
make RDBMSs useful!
Sharding is operationally obnoxious.
If we don’t need relational features, we want a
distributed NRDBMS.
4. Closed-source NRDBMSs
“The Inspiration”
Google BigTable
Applications: webtable, Reader, Maps, Blogger,
etc.
Amazon Dynamo
Shopping Cart, ?
Yahoo! PNUTS
Applications: ?
5. Data Interfaces
“This is the NOSQL meetup, right?”
Every row has a key (PK)
Key/value get/put
multiget/multiput
Range scan? With predicate pushdown?
MapReduce?
SQL?
7. Assumptions - Data Size
The data does not fit on one node.
The data may not fit on one rack.
SANs are too expensive.
Conclusion:
The system must partition its data across many
nodes.
8. Assumptions - Reliability
The system must be highly available to serve
web (and other) applications.
Since the system runs on many nodes, nodes
will crash during normal operation.
Data must be safe even though disks and
nodes will fail.
Conclusion:
The system must replicate each row to multiple
nodes and remain available despite certain node and
disk failure.
9. Assumptions - Performance
...and price thereof
All systems we’re talking about today are
meant for real-time use.
95th or 99th percentile is more important than
average latency
Commodity hardware and slow disks.
Conclusion:
The system needs to perform well on commodity
hardware, and maintain low latency even during
recovery operations.
11. Partitioning Schemes
“Where does a key live?”
Given a key, we need to determine which
node(s) it belongs on.
If that node is down, we need to find another
copy elsewhere.
Difficulties:
Unbounded number of keys.
Dynamic cluster membership.
Node failures.
14. Consistency Models
A consistency model determines rules for
visibility and apparent order of updates.
Example:
Row X is replicated on nodes M and N
Client A writes row X to node N
Some period of time t elapses.
Client B reads row X from node M
Does client B see the write from client A?
Consistency is a continuum with tradeoffs
15. Strict Consistency
All read operations must return the data from
the latest completed write operation, regardless
of which replica the operations went to
Implies either:
All operations for a given row go to the same node
(replication for availability)
or nodes employ some kind of distributed
transaction protocol (eg 2 Phase Commit or Paxos)
CAP Theorem: Strict Consistency can’t be
achieved at the same time as availability and
partition-tolerance.
16. Eventual Consistency
As t → ∞, readers will see writes.
In a steady state, the system is guaranteed to
eventually return the last written value
For example: DNS, or MySQL Slave
Replication (log shipping)
Special cases of eventual consistency:
Read-your-own-writes consistency (“sent mail”
box)
Causal consistency (if you write Y after reading X,
anyone who reads Y sees X)
gmail has RYOW but not causal!
17. Timestamps and Vector Clocks
Determining a history of a row
Eventual consistency relies on deciding what
value a row will eventually converge to
In the case of two writers writing at “the
same” time, this is difficult
Timestamps are one solution, but rely on
synchronized clocks and don’t capture causality
Vector clocks are an alternative method of
capturing order in a distributed system
18. Vector Clocks
Definition:
A vector clock is a tuple {t1 , t2 , ..., tn } of clock
values from each node
v1 < v2 if:
For all i, v1i ≤ v2i
For at least one i, v1i < v2i
v1 < v2 implies global time ordering of events
When data is written from node i, it sets ti to
its clock value.
This allows eventual consistency to resolve
consistency between writes on multiple replicas.
19. Data Models
What’s in a row?
Primary Key → Value
Value could be:
Blob
Structured (set of columns)
Semi-structured (set of column families with
arbitrary columns, eg linkto:<url> in webtable)
Each has advantages and disadvantages
Secondary Indexes? Tables/namespaces?
20. Multi-Version Storage
Using Timestamps for a 3rd dimension
Each table cell has a timestamp
Timestamps don’t necessarily need to
correspond to real life
Multiple versions (and tombstones) can exist
concurrently for a given row
Reads may return “most recent”, “most recent
before T”, etc. (free snapshots)
System may provide optimistic concurrency
control with compare-and-swap on timestamps
21. Storage Layouts
How do we lay out rows and columns on disk?
Determines performance of different access
patterns
Storage layout maps directly to disk access
patterns
Fast writes? Fast reads? Fast scans?
Whole-row access or subsets of columns?
22. Row-based Storage
Pros:
Good locality of access (on disk and in cache) of
different columns
Read/write of a single row is a single IO operation.
Cons:
But if you want to scan only one column, you still
read all.
23. Columnar Storage
Pros:
Data for a given column is stored sequentially
Scanning a single column (eg aggregate queries) is
fast
Cons:
Reading a single row may seek once per column.
24. Columnar Storage with Locality Groups
Columns are organized into families (“locality
groups”)
Benefits of row-based layout within a group.
Benefits of column-based - don’t have to read
groups you don’t care about.
25. Log Structured Merge Trees
aka “The BigTable model”
Random IO for writes is bad (and impossible in
some DFSs)
LSM Trees convert random writes to sequential
writes
Writes go to a commit log and in-memory
storage (Memtable)
The Memtable is occasionally flushed to disk
(SSTable)
The disk stores are periodically compacted
P. E. O’Neil, E. Cheng, D. Gawlick, and E. J. O’Neil. The log-structured merge-tree
(LSM-tree). Acta Informatica. 1996.
32. Cluster Management
Clients need to know where to find data
(consistent hashing tokens, etc)
Internal nodes may need to find each other as
well
Since nodes may fail and recover, a
configuration file doesn’t really suffice
We need a way of keeping some kind of
consistent view of the cluster state
33. Omniscient Master
When nodes join/leave or change state, they
talk to a master
That master holds the authoritative view of the
world
Pros: simplicity, single consistent view of the
cluster
Cons: potential SPOF unless master is made
highly available. Not partition-tolerant.
34. Gossip
Gossip is one method to propagate a view of
cluster status.
Every t seconds, on each node:
The node selects some other node to chat with.
The node reconciles its view of the cluster with its
gossip buddy.
Each node maintains a “timestamp” for itself and
for the most recent information it has from every
other node.
Information about cluster state spreads in
O(lgn) rounds (eventual consistency)
Scalable and no SPOF, but state is only
eventually consistent
41. Scalability and Reliability
What are the scaling bottlenecks? How does it
react when overloaded?
Are there any single points of failure?
When nodes fail, does the system maintain
availability of all data?
Does the system automatically re-replicate
when replicas are lost?
When new nodes are added, does the system
automatically rebalance data?
42. Performance
What’s the goal? Batch throughput or request
latency?
How many seeks for reads? For writes? How
many net RTTs?
What 99th percentile latencies have been
measured in practice?
How do failures impact serving latencies?
What throughput has been measured in
practice for bulk loads?
43. Consistency
What consistency model does the system
provide?
What situations would cause a lapse of
consistency, if any?
Can consistency semantics be tweaked by
configuration settings?
Is there a way to do compare-and-swap on row
contents for optimistic locking? Multirow?
44. Cluster Management and Topology
Does the system have a single master? Does it
use gossip to spread cluster management data?
Can it withstand network partitions and still
provide some level of service?
Can it be deployed across multiple datacenters
for disaster recovery?
Can nodes be commissioned/decomissioned
automatically without downtime?
Operational hooks for monitoring and metrics?
45. Data Model and Storage
What data model and storage system does the
system provide?
Is it pluggable?
What IO patterns does the system cause under
different workloads?
Is the system best at random or sequential
access? For read-mostly or write-mostly?
Are there practical limits on key, value, or row
sizes?
Is compression available?
46. Data Access Methods
What methods exist for accessing data? Can I
access it from language X?
Is there a way to perform filtering or selection
at the server side?
Are there bulk load tools to get data in/out
efficiently?
Is there a provision for data backup/restore?
47. Real Life Considerations
(I was talking about fake life in the first 45 slides)
Who uses this system? How big are the
clusters it’s deployed on, and what kind of load
do they handle?
Who develops this system? Is this a community
project or run by a single organization? Are
outside contributions regularly accepted?
Who supports this system? Is there an active
community who will help me deploy it and
debug issues? Docs?
What is the open source license?
What is the development roadmap?