We have a lot of user interaction data from various websites stored in Cassandra such as cookies, page-visits, ads-viewed, ads-clicked, etc.. that we would like to do reporting on. Our current Cassandra schema supports basic reporting and querying. However we also would like to build large queries that would typically involve Joins on large Column Families (containing millions of rows).
What approach is best suited for this? One possibility is to extract data out to a relational database such as mySQL and do data mining there. Alternate could be to attempt at use hadoop with hive or pig to run map reduce queries for this purpose? I must admit I have zero experience with the latter.
Anyone have experience of performance differences in one one vs the other? Would you run map reduce queries on a live Cassandra production instance or on a backup copy to prevent query load from affecting write performance?
In my experience Cassandra is better suited to processes where you need real-time access to your data, fast random reads and just generally handle large traffic loads. However, if you start doing complex analytics, the availability of your Cassandra cluster will probably suffer noticeably. In general from what I've seen it's in your best interest to leave the Cassandra cluster alone, otherwise the availability starts suffering.
Sounds like you need an analytics platform, and I would definitely advise exporting your reporting data out of Cassandra to use in an offline data-warehouse system.
If you can afford it, having a real data-warehouse would allow you to do complex queries with complex joins on multiples tables. These data-warehouse systems are widely used for reporting, here is a list of what are in my opinion the key players:
Netezza
Aster/TeraData
Vertica
A recent one which is gaining a lot of momentum is Amazon Redshift, but it is currently in beta, but if you can get your hands on it you could give this a try since it looks like a solid analytics platform with a pricing much more attractive than the above solutions.
Alternatives like using Hadoop MapReduce/Hive/Pig are also interesting to look at, but probably not a replacement for Hadoop technologies. I would recommend Hive if you have a SQL background because it will be very easy to understand what you're doing and you can scale easily. There are actually already libraries integrated with Hadoop, like Apache Mahout, which allow you to do data-mining on a Hadoop cluster, you should definitely give this a try and see if it fits your needs.
To give you an idea, an approach that I've used that has been working well so far is pre-aggregating the results in Hive and then have the reports themselves generated in a data-warehouse like Netezza to compute complex joins .
Disclosure: I'm an engineer at DataStax.
In addition to Charles' suggestions, you might want to look into DataStax Enterprise (DSE), which offers a nice integration of Cassandra with Hadoop, Hive, Pig, and Mahout.
As Charles mentioned, you don't want to run your analytics directly against Cassandra nodes that are handling your real-time application needs because they can have a substantial impact on performance. To avoid this, DSE allows you to devote a portion of your cluster strictly to analytics by using multiple virtual "datacenters" (in the NetworkToplogyStrategy sense of the term). Queries performed as part of a Hadoop job will only impact those nodes, essentially leaving your normal Cassandra nodes unaffected. Additionally, you can scale each portion of the cluster up or down separately based on your performance needs.
There are a couple of upsides to the DSE approach. The first is that you don't need to perform any ETL prior to processing your data; Cassandra's normal replication mechanisms keep the nodes devoted to analytics up to date. Second, you don't need an external Hadoop cluster. DSE includes a drop-in replacement for HDFS called CFS (CassandraFS), so all source data, intermediate results, and final results from a Hadoop job can be stored in the Cassandra cluster.
Related
So like most Enterprise companies, we have built a data warehouse in Hadoop, with user queries supported in Hive, and now after a few months and user acceptance testing everyone is a little surprised about how it is not like a standard (Oracle/Netezza) database when used by end-users for ad-hoc data analysis.
While I understand that this is probably a very stupid way of doing projects (we should have researched the use cases and best fit technologies before building the product), and I know the basic technical aspects of how Hadoop differs from single node machines... I would still want to understand if using Hadoop/Hive makes sense for data warehouses in any scenario?
For instance,
Are there always trade-offs in query performance or can they be optimized with configuration changes, horizontal scaling of hardware?
Can it ever be as fast as something like Netezza - which uses non-commodity hardware but functions on a similar architecture?
Where is Hadoop great and absolutely defeats everything else in comparison?
I would argue the Hive MetaStore is useful more than HiveServer2 itself as the query interface.
The MetaStore is what Presto and Spark use to get data much quicker than MapReduce, but maybe not as fast as a well-optimized Tez query, and improvements are being made in Hive v2.x+ with LLAP, for example.
In the end, Hive is really only useful if the ingestion pipelines are actually storing the data in columnar formats of ORC or Parquet to begin with. From there, and reasonable query engine can scan through that data fairly quickly, and Hive just happens to be considered the defacto implementation of that access pattern, whereas Impala or Presto are often more used for adhoc access.
That being said, Hive (and other SQL on Hadoop) is not used for "building", it is used for "analyzing"
And I don't know what you mean by "standard" - Hive supports any ODBC/JDBC Connection, so it's not like you go to the CLI for all access, and HUE or Zeppelin make really nice notebooks for SQL analysis over Hive.
To answer your question,
Are there always trade-offs in query performance or can they be optimized with configuration changes, horizontal scaling of hardware?
If you are using only hive tool from Hadoop for Adhoc querying then that is not right choice for adhoc querying and data analysis. We have explore better option according to you use case and make tech selection from Hive LLAP, HBase, Spark, SparkSQL, Spark Streaming, Apache storm, Imapala, Apache Drill and Prestodb etc.
Can it ever be as fast as something like Netezza - which uses non-commodity hardware but functions on a similar architecture?
It is better tool now days most of organization using but you have to be specific about tech tools selection from Hadoop tech stack according to you use case and after studying it do right selection for technology.
Where is Hadoop great and absolutely defeats everything else in comparison?
Hadoop is best for implementing data lake platform in large organization where data scattered across multiple systems, and using Hadoop data lake you can have data at center place. Which can leveraged as data analytics platform for organization data which accumulated over the time period. Also can be used for data stream data processing to get results in real time.
Hope this will help.
Well, there are many benefits of using storing big data in HDFS or say Hadoop ecosystem. To name the most important ones, someone is there who can store and process huge data and the configuration is pretty straight forward.
I can't wrap my head around the basic theoretical concept of 'Operational and Analytical Big Data'.
According to me:
Operational Big Data: Branch where we can perform Read/write operations on big data using specially designed Databases (NoSQL). Somewhat similar to ETL in RDMS.
Analytical Big Data: Branch where we analyse data in retrospect and draw predictions using techniques like MPP and MapReduce. Somewhat similar to reporting in RDMS.
(Please feel free to correct wherever I'm wrong, it's just my understanding.)
So according to me, Hadoop is used for Analytical Big Data where we just process data for analysis but don't temper original data and hence is not an idea choice for ETL.
But recently I have come across this article which advocates using Hadoop for ETL: https://www.datanami.com/2014/09/01/five-steps-to-running-etl-on-hadoop-for-web-companies/
Hadoop (MapReduce) is not an efficient processing layer, IMO, without adequate tweaking, so out of the box, the answer is neither. Sure, MapReduce could be used, and under the hood, that API is what most higher level tools depend on, but since those other tools exist, you wouldn't want to go write ETL jobs in plain MapReduce.
You can combine Hadoop with Spark, Presto, HBase, Hive, etc. to unlock these other Operational or Analytical layers, some are useful for reporting use cases, and others are useful for ETL. Again, plenty of knobs to get useful results in a reasonable time compared to an RDBMS (or other NoSQL tools). Plus, it takes several attempts to know how to best store data in Hadoop to begin with (hint: not plaintext, and not lots of small files)
That link is over 5 years old now, and references Flume and Sqoop. Other "web scale" technologies have shown their worth in that time, meanwhile Flume and Sqoop have shown their age can be difficult to configure manage compared to tools like Apache NiFi.
I am planning to spin my development cluster for trend analysis for Infrastructure Monitoring application which I am planning to build using Spark for analysing failure trend and Cassandra for storing incoming data and analysed data.
Consider collecting performance matrix from around 25000 machines/servers (probably set of same application on different servers). I am expecting performance matrix of size 2MB/sec from each machine, which I am planning to push into Cassandra table having timestamp, server as primary key and application along with some important matrix as clustering key. I will be running Spark job on top of this stored information for performance matrix failure trend analysis.
Comming to the question, How many nodes (machines) and of what configuration in terms of CPU and Memory do I need to kick start my cluster considering above scenario.
Cassandra needs a well planned out data model for things to run well. It is very much worth spending time planning things out at this stage before you have a large data set and find out you probably would have done better re-arranging the data model!
The "general" rule of thumb is you shape your model to the queries, while paying attention to avoiding things like really large rows, large deletes, batches and such the like which can have big performance penalties.
The docs give a good start on planning and testing you would probably find useful. I would also recommend using the Cassandra stress tool. You can use it to push performance tests into your Cassandra cluster to check latencies and any performance problems. You can use your own schema too which I personally think is super-useful!
If you are using cloud based hardware like AWS then its relatively easy to scale up / down and see what works best for you. You dont need to throw big hardware at Cassandra, its easier to scale horizontally than vertically.
I'm assuming you are pulling back the data into a separate spark cluster for the analytics side too so these nodes would be running plain Cassandra (less hardware specs). If however you are using the Datastax Enterprise version (where you can run nodes in spark "mode") then you will need more beefier hardware with the additional load you need for spark driver programs, executors and such the like. Another good docs link is the DSE hardware recommendations
I want to know the advantages/disadvantages of using a MySQL Cluster and using the Hadoop framework.
What is the better solution. I would like to read your opinion.
I think the advantages of using a MySQL Cluster are:
high availability
good scalability
high performance / real time data access
you can use commodity hardware
And I don't see a disadvantage! Are there any disadvantages that Hadoop do not has?
The advantages of Hadoop with Hive on top of it are:
also good scalability
you can also use commodity hardware
the ability to run in heterogenous environments
parallel computing with the MapReduce framework
Hive with HiveQL
and the disadvantage is:
no real time data access. It may takes minutes or hours to analyze the data.
So in my opinion for handling big data a MySQL cluster is the better solution. Why Hadoop is the holy grail of handling big data? What is your opinion?
Both of the above answers miss a huge differentiation between mySQL and Hadoop. mySQL requires you to store data in a certain format. It likes heavily structured data - you declare the data type of each column in a table etc. Hadoop doesn't care about this at all.
Example - if you have a billion text log files, to make analysis even possible for mySQL you'd need to parse and load the data first into a mySQL table, typeing each column along the way. With hadoop and mapreduce, you define the function that is to scan/analyze/return the data from its raw source - you don't need pre-processing ETL to get it pre-structured.
If the data is already structured and in mySQL - then (hopefully) its well structured - why export it for hadoop to analyze? If it isn't, why spend the time to ETL the data?
Hadoop is not a replacement of MySQL, so I think they have their own scenario。
Every one know hadoop is better for batch job or offline compute, but there also have many related real time product, such as hbase.
If you wanna choose a offline compute & storage arch.
I suggest hadoop not MySQL cluster for offline compute & storage, because of :
Cost : obviously, hadoop cluster is more cheap than MySQL cluster
Scalability : hadoop support more than ten thousands machine in a cluster
Ecosystem : mapreduce, hive, pig, sqoop and etc.
So you can choose hadoop as offline compute & storage and MySQL as online compute & storage, you also can learn more from lambda architecture.
The other answer is good, but doesn't really explain why hadoop is more scalable for offline data crunching than MySQL Clusters. Hadoop is more efficient for large data sets that must be distributed across many machines because it gives you full control over the sharding of data.
MySQL clusters use auto-sharding, and it's designed to randomly distribute the data so no one machine gets hit with more of the load. On the other hand, Hadoop allows you to explicitly define the data partition so that multiple data points that require simultaneous access will be on the same machine, minimizing the amount of communication among the machines necessary to get the job done. This makes Hadoop better for processing massive data sets in many cases.
The answer to this question has a good explanation of this distinction.
What is the point in feeding an Hadoop cluster and using that cluster to feed data into a Vertica/InfoBright datawarehouse ?
All thse vendor keep saying "we can connect with Hadoop", but I don't understand what's the point. What is the interest of storing in Hadoop and transfering into InfoBright ? Why not have the applications store directly in the Infobright/Vertica DW ?
Thank you !
Why combine the solutions? Hadoop has some great capabilities (see url below). These capabilities though do not include allowing business users to run quick analytics. Queries that take 30 minutes to hours in Hadoop are being delivered in 10’s of seconds with Infobright.
BTW, your initial question did not presuppose an MPP architecture and for good reason. Infobright customers Liverail, AdSafe Media & InMobi, among others, utilize IEE with Hadoop.
If you register for an Industry White Paper http://support.infobright.com/Support/Resource-Library/Whitepapers/ you will see a view of the current marketplace where four suggested Use Cases for Hadoop are outlined. It was authored by Wayne Eckerson , Director of Research, Business Applications and Architecture Group, TechTarget, in September 2011.
1) Create an online archive.
With Hadoop, organizations don’t have to delete or ship the data to offline storage; they can keep it online indefinitely by adding commodity servers to meet storage and processing requirements. Hadoop becomes a low-cost alternative for meeting online archival requirements.
2) Feed the data warehouse.
Organizations can also use Hadoop to parse, integrate and aggregate large volumes of Web or other types of data and then ship it to the data warehouse, where both casual and power users can query and analyze the data using familiar BI tools. Here, Hadoop becomes an ETL tool for processing large volumes of Web data before it lands in the corporate data warehouse.
3) Support analytics.
The big data crowd (i.e., Internet developers) views Hadoop primarily as an analytical engine for running analytical computations against large volumes of data. To query Hadoop, analysts currently need to write programs in Java or other languages and understand MapReduce, a framework for writing distributed (or parallel) applications. The advantage here is that analysts aren’t restricted by SQL when formulating queries. SQL does not support many types of analytics, especially those that involve inter-row calculations, which are common in Web traffic analysis. The disadvantage is that Hadoop is batch-oriented and not conducive to iterative querying.
4) Run reports.
Hadoop’s batch-orientation, however, makes it suitable for executing regularly scheduled reports. Rather than running reports against summary data, organizations can now run them against raw data, guaranteeing the most accurate results.
There are several reasons you may want to do that
1. Cost per TB. The storage costs in Hadoop are much cheaper than Vertica/Netezza/greenplum and the like). You can get long-term retention in Hadoop and shorter term data in the analytics DB
2. Data ingestion capabilities in hadoop (performing transformations) is better in Hadoop
3. programatic analytics (libraries like Mahout ) so you can build advanced text analytics
4. dealing with unstructured data
The MPP dbs provide better performance in ad-hoc queries, better dealing with structured data and connectivity to traditional BI tools (OLAP and reporting) - so basically Hadoop complements the offering of these DBs
Hadoop is more of a platform than a DB.
Think of Hadoop as a neat filesystem that supports lots of queries over different of file types. With this in mind, most people dump raw data onto Hadoop and use it as a staging layer in the data pipeline, where it can chew the data and push it to other systems like vertica or any other. You have several advantages that can be resumed to decoupling.
So Hadoop is turning into the facto storage platform for big data. It is simple, fault-tolerant, scales well, and it is easy to feed and to get data out of it. So most vendors are trying to push a product to companies that probably have a Hadoop installation.
What makes the joint deployment so effective for this software ?
First, both platforms have a lot in common:
Purpose-built from scratch for Big Data transformation and analytics
Leverage MPP architecture to scale out with commodity hardware,
capable of managing TBs through PBs of data
Native HA support with low administration overhead
Hadoop is ideal for the initial exploratory data analysis, where the data is often available in HDFS and is schema-less, and batch jobs usually suffice, whereas Vertica is ideal for stylized, interactive analysis, where a known analytic method needs to be applied repeatedly to incoming batches of data.
By using Vertica’s Hadoop connector, users can easily move data between the two platforms. Also, a single analytic job can be decomposed into bits and pieces that leverage the execution power of both platforms; for instance, in a web analytics use case, the JSON data generated by web servers is initially dumped into HDFS. A map-reduce job is then invoked to convert such semi-structured data into relational tuples, with the results being loaded into Vertica for optimized storage and retrieval by subsequent analytic queries.
What are the Key differences that make Hadoop and Vertica complement each other when addressing Big Data.
Interface and extensibility
Hadoop
Hadoop’s map-reduce programming interface is designed for developers.The platform is acclaimed for its multi-language support as well as ready-made analytic library packages supplied by a strong community.
Vertica
Vertica’s interface complies with BI industry standards (SQL, ODBC, JDBC etc). This enables both technologists and business analysts to leverage Vertica in their analytic use cases. The SDK is an alternative to the map-reduce paradigm, and often delivers higher performance.
Tool chain/Eco system
Hadoop
Hadoop and HDFS integrate well with many other open source tools. Its integration with existing BI tools is emerging.
Vertica
Vertica integrates with the BI tools because of its standards compliant interface. Through Vertica’s Hadoop connector, data can be exchanged in parallel between Hadoop and Vertica.
Storage management
Hadoop
Hadoop replicates data 3 times by default for HA. It segments data across the machine cluster for loading balancing, but the data segmentation scheme is opaque to the end users and cannot be tweaked to optimize for the analytic jobs.
Vertica
Vertica’s columnar compression often achieves 10:1 in its compression ratio. A typical Vertica deployment replicates data once for HA, and both data replicas can attain different physical layout in order to optimize for a wider range of queries. Finally, Vertica segments data not only for load balancing, but for compression and query workload optimization as well.
Runtime optimization
Hadoop
Because the HDFS storage management does not sort or segment data in ways that optimize for an analytic job, at job runtime the input data often needs to be resegmented across the cluster and/or sorted, incurring a large amount of network and disk I/O.
Vertica
The data layout is often optimized for the target query workload during data loading, so that a minimal amount of I/O is incurred at query runtime. As a result, Vertica is designed for real-time analytics as opposed to batch oriented data processing.
Auto tuning
Hadoop
The map-reduce programs use procedural languages (Java, python, etc), which provide the developers fine-grained control of the analytic logic, but also requires that the developers optimize the jobs carefully in their programs.
Vertica
The Vertica Database Designer provides automatic performance tuning given an input workload. Queries are specified in the declarative SQL language, and are automatically optimized by the Vertica columnar optimizer.
I'm not a Hadoop user (just a Vertica user/DBA), but I would assume the answer would be something along these lines:
-You already have a setup using Hadoop and you want to add a "Big Data" database for intensive analytical analysis.
-You want to use Hadoop for non-analytical functions and processing and a database for analysis. But it is the same data, so no need for two feeds.
To expand slightly on Arnon's answer, Hadoop has been recognized as a force that is not going away and is gaining increasing traction in organizations, many times via grassroots efforts from developers. MPP databases are good at answering questions that we know about at design time such as "How many transactions do we get per hour by country?".
Hadoop started as a platform for a new type of developer that lives somewhere between analysts and developers, one who can write code but also understands data analysis and machine learning. MPP databases (column or not) are very poor at serving this type of developer who often is analyzing unstructured data, using algorithms that require too much CPU power to run in a database or datasets which are too large. The sheer amount of CPU power required to build some models makes running these algorithms in any sort of traditional sharded DB impossible.
My personal pipeline using hadoop typically looks like:
Run a number of very large global queries in Hadoop to get a basic feel for the data and the distribution of variables.
Use Hadoop to build a smaller dataset with just the data I am interested in.
Export the smaller dataset into a relational DB.
Run lots of small queries on the relational db, build excel sheets, sometimes do a little R.
Bear in mind that this workflow only works for the "analyst developer" or "data scientist". Others mileage will vary.
Coming back to your question due to people like me abandoning their tools these companies are looking for ways to remain relevant in an age where Hadoop is synonymous with big data, the coolest startups and cutting edge technology (whether this is earned or not you may discuss amongst yourselves.) Also many Hadoop installations are an order of magnitude or more larger than an organizations MPP deployments, meaning more data is being retained for longer in Hadoop.
Massive parallel database like Greenplum DB are excellent for handling massive amounts of structured data. Hadoop is excellent at handling even more massive amounts of unstructured data, e.g. websites.
Nowadays, a ton of interesting analytics combines these both types of data to gain insight. Therefore it is important for these database systems to be able to integrate with Hadoop.
For example you could do text processing on the Hadoop Cluster using MapReduce until you have some scoring value per product or something. This scoring value then could be used by the database to combine it with other data that is already stored in the database or data that has been loaded into the database from other sources.
Unstructured data, by their nature, is not suitable for loading into your traditional data warehouse. Hadoop mapreduce jobs can extract structures out of your log files (ex) and then the same can then be ported into your DW for analytics. Hadoop is batch processing, therefore is not suitable for analytic query processing. So you can process your data using hadoop to bring some structure, and then make it query ready via your visualization/sql layer.
What is the point in feeding an Hadoop cluster and using that cluster to feed data into a Vertica/InfoBright datawarehouse ?
The point is you would not want your users to fire up a query and wait for minutes, sometimes hours before you come back with an answer. Hadoop cannot provide you with a real time query response. Although this is changing with the advent of Cloudera's Impala and Hortonworks's Stinger. These are real-time data processing engines over Hadoop.
Hadoop's underlying data system, HDFS, allows chunking up your data and distributing it over the nodes in your cluster. In fact, HDFS can also be replaced with a 3rd party data storage like S3. Point is: Hadoop provides both -> storage + processing. So you are welcome to use hadoop as storage engine and extract the data into your data warehouse when needed. You can also use Hadoop to create cubes and marts and store these marts in the warehouse.
However, with the advent of Stinger and Impala, the strength of these claims will eventually be erased. So keep an eye out.