I am planning to build a new system in Hadoop, that brings data from External Environment and then do some transformations and builds up a end product.
The external data (if we can assume it is from either oracle/mysql/postgre-sql data base, there can be n-data bases schema) that comes to hadoop system should be always real time (new data should get inserted and updated data should get updated), may be atleast an hour delay at max (we can poll/push hourly basis).
We can also assume the data that exists in my data base schema is with n-tables, I may need m-tables only out of n-tables that exists in source. And each table data of size in GB/TB. So I can't go with full table replace. I should always go incremental(updates/inserts) push/pull into hadoop system.
Hive may support, by dividing my data into date wise partitions, and can query faster, but doesn't not support updates so I have to go for full table replace always, which does not scalable.
My end goal is "Real time data into hadoop system, read query performace, update performance".
Your Technical suggestions for my use case is very useful.
Related
There is a situation in our systems in which the user can view and "close" a report. After they close it, the report is moved to a temporary table inside the database where it is kept for 24 hrs, and then moved to an archives table(where the report is stored for next 7 years). At any point during the 7 years, a user can "reopen" the report and work on it. The problem is that archives storage is getting large and finding/reopening reports tend to be time consuming. And I need to get statistics on the archives from time to time(i.e. report dates, clients, average length "opened", etc). I want to use a big data approach but I am not sure whether to use Hadoop, Cassandra, or something else ? Can someone provide me with some guidelines how to get started and decide on what to use ?
If you archive is large and you'd like to get reports from it, you won't be able to use just Cassandra, as it has no easy means of aggregating the data. You'll end up collocating Hadoop and Cassandra on the same nodes.
From my experience archives (write once - read many) is not the best use case for Cassandra if you're having a lot of writes (we've tried it for a backend for a backup sysyem). Depending on your compaction strategy you'll pay either in space or in iops for having that. Added changes are propagated through the SSTable hierarchies resulting in a lot more writes than the original change.
It is not possible to answer your question in full without knowing other variables: how much hardware (servers, their ram/cpu/hdd/ssd) are you going to allocate? what is the size of each 'report' entry? how many reads / writes you usually serve daily? How large is your archive storage now?
Cassandra might work fine. Keep two tables, reports and reports_archive. Define the schema using a TTL of 24 hours and 7 years:
CREATE TABLE reports (
...
) WITH default_time_to_live = 86400;
CREATE TABLE reports_archive (
...
) WITH default_time_to_live = 86400 * 365 * 7;
Use the new Time Window Compaction Strategy (TWCS) to minimize write amplification. It could be advantageous to store the report metadata and report binary data in separate tables.
For roll-up analytics, use Spark with Cassandra. You don't mention the size of your data, but roughly speaking 1-3 TB per Cassandra node should work fine. Using RF=3 you'll need at least three nodes.
I have an oracle database and need to import data to a hive table. The daily import data size would be around 1 GB. What would be the better approach?
If I import each day data as a partition, how can the updated values be handled?
For example, if I imported today's data as a partition and for the next day there are some fields that are updated with the new values.
Using --lastmodified we can get the values but where we need to send the updated values to the new partition or to the old (already existing) partition?
If I send to the new partition, then the data is duplicated.
If I want to send to the already existing partition, how we can it be achieved?
Your only option is to override the entire existing partition with 'INSERT OVERWRITE TABLE...'.
Question is - how far back are you going to be constantly updating the data?
I think of 3 approaches u can consider:
Decide on a threshold for 'fresh' data. for example '14 days backwards' or '1 month backwards'.
Then each day you are running the job, you override partitions (only the ones which have updated values) backwards, until the threshold decided.
With ~1 GB a day it should be feasible.
All the data from before your decided time is not guranteed to be 100% correct.
This scenario could be relevant if you know the fields can only be changed a certain time window after they were initially set.
Make your Hive table compatible with ACID transactions, thus allowing updates on the table.
Split your daily job to 2 tasks: the new data being written for the run day. the updated data that you need to run backwards. the sqoop will be responsible for the new data. take care of the updated data 'manually' (some script that generates the update statements)
Don't use partitions based on time. maybe dynamic partitioning is more suitable for your use case.It depends on the nature of the data being handled.
I would like to store a large amount of timeseries from devices. Also these timeseries have to be validated, can be modified by an operator and have to be exported to other systems. Holes in the timeseries must be found. Timeseries must be shown in the UI filtered by serialnumber and date range.
We have thought about using hadoop, hbase, opentsdb and spark for this scenario.
What do you think about it? Can Spark connect to opentsdb easily?
Thanks
OpenTSDB is really great for storing large amount of time series data. Internally, it is underpinned by HBase - which means that it had to find a way around HBase's limitations in order to perform well. As a result, the representation of time series is highly optimized and not easy to decode. AFAIK, there is no out-of-the-box connector that would allow to fetch data from OpenTSDB into Spark.
The following GitHub project might provide you with some guidance:
Achak1987's connector
If you are looking for libs that would help you with time series, have a look at spark-ts - it contains useful functions for missing data imputation as well.
Warp 10 offers the WarpScript language which can be used from Spark/Pig/Flink to manipulate time series and access data stored in Warp 10 via a Warp10InputFormat.
Warp 10 is Open Source and available at www.warp10.io
Disclaimer: I'm CTO of Cityzen Data, maker of Warp 10.
Take a look at Axibase Time Series Database which has a rather unique versioning feature to maintain a history of value changes for the same timestamp. Once enabled with per-metric granularity, the database keeps track of source, status and times of value modifications for audit trail or data reconciliation.
We have customers streaming data from Spark apps using Network API, typically once data is enriched with additional metadata (aks series tags) for downstream reporting.
You can query data from ATSD with REST API or SQL.
Disclaimer: I work for Axibase.
I would like to implement a synchronization between a source SQL base database and a target TripleStore.
However for matter of simplicity let say simply 2 databases. I wonder what approaches to use to have every change in the source database replicated in the target database. More specifically, I would like that each time some row changes in the source database that this can be seen by a process that will read the changes and populate the target database accordingly while applying some transformation in the middle.
I have seen suggestion around the mechanism of notification that can
be available in the database, or building tables such that changes can
be tracked (meaning doing it manually) and have the process polling it
at different intervals, or the usage of Logs (change data capture,
etc...)
I'm seriously puzzle about all of this. I wonder if anyone could give some guidance and explanation about the different approaches with respect to my objective. Meaning: name of methods and where to look.
My organization mostly uses: Postgres and Oracle database.
I have to take relational data and transform them in RDF so as to store them in a triplestore and keep that triplestore constantly synchronized with the data is the SQL Store.
Please,
Many thanks
PS:
A clarification between ETL and replication techniques as in Change Data capture, with respect to my overall objective would be appreciated.
Again i need to make sense of the subject, know what are the methods, so i can further start digging for myself. So far i have understood that CDC is the new way to go.
Assuming you can't use replication and you need to use some kind of ETL process to actually extract, transform and load all changes to the destination database, you could use insert, update and delete triggers to fill a (manually created) audit table. Columns GeneratedId, TableName, RowId, Action (insert, update, delete) and a boolean value to determine if your ETL process has already processed this change. Use that table to get all the changed rows in your database and transport them to the destination database. Then delete the processed rows from the audit table so that it doesn't grow too big. How often you have to run the ETL process depends on the amount of changes occurring in the source database.
I'm having fun learning about Hadoop and the various projects around it and currently have 2 different strategies I'm thinking about for building a system to store a large collection of market tick data, I'm just getting started with both Hadoop/HDSF and HBase but hoping someone can help me plant a system seed that I won't have to junk later using these technologies. Below is an outline of my system and requirements with some query and data usage use cases and lastly my current thinking about the best approach from the little documentation I have read. It is an open ended question and I'll gladly like any answer that is insightful and accept the best one, feel free to comment on any or all of the points below. - Duncan Krebs
System Requirements - Be able to leverage the data store for historical back testing of systems, historical data charting and future data mining. Once stored, data will always be read-only, fast data access is desired but not a must-have when back testing.
Static Schema - Very Simple, I want to capture 3 types of messages from the feed:
Timestamp including date,day,time
Quote including Symbol,timestamp,ask,askSize,bid,bidSize,volume....(About 40 columns of data)
Trade including Symbol,timestamp,price,size,exchange.... (About 20 columns of data)
Data Insert Use Cases - Either from a live market stream of data or lookup via broker API
Data Query Use Cases - Below demonstrates how I would like to logically query my data.
Get me all Quotes,Trades,Timestamps for GOOG on 9/22/2014
Get me all Trades for GOOG,FB BEFORE 9/1/2014 AND AFTER 5/1/2014
Get me the number of trades for these 50 symbols for each day over the last 90 days.
The Holy Grail - Can MapReduce be used for uses cases like these below??
Generate meta-data from the raw market data through distributed agents. For example, Write a job that will compute the average trading volume on a 1 minute interval for all stocks and all sessions stored in the database. Create the job to have an agent for each stock/session that I tell what stock and session it should compute this value for. (Is this what MapReduce can do???)
On the classpath of the agents can I add my own util code so that the use case above for example could publish its value into a central repo or Messaging server? Can I deploy an agent as an OSGI bundle?
Create different types of agents for different types of metrics and scores that are executed every morning before pre-market trading?
High Frequency Trading
I'm also interested if anyone can share some experience using Hadoop in the context of high frequency trading systems. Just getting into this technology my initial sense is Hadoop can be great for storing and processing large volumes of historic tick data, if anyone is using this for real-time trading I'd be interested in learning more! - Duncan Krebs
Based of my understanding of your requirements, Hadoop would be really good solution to store your data and run your queries on it using Hive.
Storage: You can store the data in Hadoop in a directory structure like:
~/stock_data/years=2014/months=201409/days=20140925/hours=01/file
Inside the hours folder, the data specific to that hour of the day can reside.
One advantage of using such structure is that you can create external tables in Hive over this data with your partitions on years, months, days and hours. Something like this:
Create external table stock_data (schema) PARTITIONED BY (years bigint, months bigint, days bigint, hours int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' LOCATION
'~/stock_data'
Coming to the queries part, once you have the data stored in the format mentioned above you can easily run simple queries.
Get me all Quotes,Trades,Timestamps for GOOG on 9/22/2014
select * from stock_data where stock = 'GOOG' and days = 20140922
Get me all Trades for GOOG,FB BEFORE 9/1/2014 AND AFTER 5/1/2014
select * from stock_data where stock in ('GOOG', 'FB') and days > 20140501 and days < 20140901)
You can run any such aggregation queries once in a day and use the output to come up with the metrics before pre-market trading. Since Hive internally runs mapreduce these queries won't be very fast.
In order to get faster results, you can use some of the in memory projects like Impala or Spark. I have myself used Impala to run queries on my hive tables and I have seen a major improvement in the run time for my queries (around 40x). Also you wouldn't need to make any changes to the structure of the data.
Data Insert Use Cases : You can use tools like Flume or Kafka for inserting data in real time to Hadoop (and thus to the hive tables). Flume is linearly scalable and can also help in processing events on the fly while transferring.
Overall, a combination of multiple big data technologies can provide a really decent solution to the problem you proposed and these solution would scale to huge amounts of data.