Vertica by the Hour, Red Hat
Micro Focus | 10.1.0-3Linux/Unix, Red Hat Enterprise Linux 7.5 - 64-bit Amazon Machine Image (AMI)
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Great product
What do you like best?
HPE is one of the few solutions in their space that actually care about making an effective solution for their customers. Most companies just want the flashy new buzz words, but HPE prioritizes the real needs of their customers.
What do you dislike?
There were no particular things I disliked
What problems are you solving with the product? What benefits have you realized?
Moved from Oracle to HPE Vertica, saved massive amounts of money on infrastructure and allowed us to scale.
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Incredible
What do you like best?
Before I met HP Vertica, I've worked with a few other column-store databases.
Few were easy to install and use, but were not reliable; others had good performance and stability but were definitely not use to deploy.
After 2-3 hours of deciding to try Vertica, I was running queries on it.
So what I like the best:
- Easy to deploy;
- Easy to Configure;
- Plenty of native tools to help the maintenance;
- Plenty of *clear* documentation to help you though;
- Fast - hands-down, the fastest for hardware I've worked with;
- Small - incredible compression rates to the data;
- Reliable/Stable fault-tolerant and - in my case - no data loss issues;
- No need for planning, analyzing or maintaining table indexes;
- Familiar way of loading data [PostgreSQL-like / COPY]
Few were easy to install and use, but were not reliable; others had good performance and stability but were definitely not use to deploy.
After 2-3 hours of deciding to try Vertica, I was running queries on it.
So what I like the best:
- Easy to deploy;
- Easy to Configure;
- Plenty of native tools to help the maintenance;
- Plenty of *clear* documentation to help you though;
- Fast - hands-down, the fastest for hardware I've worked with;
- Small - incredible compression rates to the data;
- Reliable/Stable fault-tolerant and - in my case - no data loss issues;
- No need for planning, analyzing or maintaining table indexes;
- Familiar way of loading data [PostgreSQL-like / COPY]
What do you dislike?
Even though most of the problems I've had were already answered in public forums from the Vertica Communiy - I use the community version -, I feel like there's not a big crowd trying to help or at least trying to make their voice heard online.
What problems are you solving with the product? What benefits have you realized?
Using the Vertica Community Version, we were able to increase - sometimes by a factor of 10 - the speed of Data Warehouses queries.
As we didn't have much time to try tens of different databases, Vertica showed handy when it comes to Install/Loading data.
As we didn't have much time to try tens of different databases, Vertica showed handy when it comes to Install/Loading data.
Recommendations to others considering the product:
Read all the documents you can about Hardware Requirements. Even though it looks big, it's a very complete document, every DBA should beware of.
Because Vertica looks from outside a lot like PostgreSQL, users tend to forget some of its specificities when writing SQL queries or doing some other maintenance tasks. So beware of subqueries, data types, row-inserts - prefer bulk inserts - table/column drops, etc.
Because Vertica looks from outside a lot like PostgreSQL, users tend to forget some of its specificities when writing SQL queries or doing some other maintenance tasks. So beware of subqueries, data types, row-inserts - prefer bulk inserts - table/column drops, etc.
Used for batch logs analysis
What do you like best?
It presents a relational DB feel but works under the hood as a columnar store. We achieved two orders-of-magnitude over several other tools and eliminated the need for pivot tables entirely. The polish and easy of setup also played a big factor in getting me to use it, even though I generally favor open-source solutions for holding data.
What do you dislike?
It's not open-source. I prefer to store data long-term in open-source solutions to avoid lock-in.
What problems are you solving with the product? What benefits have you realized?
It was fast enough to build a dashboard on top of while facilitating many huge simultaneous queries without need for pivot tables. It saved us huge amount of man hours as we didn't have to maintain partial queries in tables for represent different views on data.
Recommendations to others considering the product:
If you can afford it, use it. Or if you can keep your internal DB lower than 1TB use that.
Vertica: A fast and cost effective database for handling Big Data
What do you like best?
It is fast and I have been told it is easy to install. Adding and removing nodes is also very painless. It's a RDBMS which can scale easily. User Defined Functions can be written to bring additional functionality. It can also be integrated with Hadoop and the data can be used to run MapReduce jobs.
What do you dislike?
It's not open source and is owned by HP. So it's a paid distribution and its future is dependent on HP.
What problems are you solving with the product? What benefits have you realized?
We wanted a database for handling Big Data. We started with Hive but it converted its queries to multiple MapReduce jobs. In Hadoop, after each subsequent MapReduce job, data is written to disk. This slows down the queries by a large margin.
Recommendations to others considering the product:
I would recommend you check Hive before you jump to this. I have been told that it has made good progress in the past few years.
Vertica , and its Stability
What do you like best?
At my company, we use vertica for data that has to ensure 100% uptime, such as payments, and other monetary processing.
Since there is major redundancy, unless a major datacenter outage, vertica is very stable and rarely goes down fully.
Since there is major redundancy, unless a major datacenter outage, vertica is very stable and rarely goes down fully.
What do you dislike?
I dislike the UI.
I find it clunky and tends to be anti cognitive.
I find it clunky and tends to be anti cognitive.
What problems are you solving with the product? What benefits have you realized?
We use it for the mobile monetary processing. It has been amazing, and works clean.
Recommendations to others considering the product:
Better UI
Vertica as a Lightening Fast BI Data Source
What do you like best?
Vertica is truly the fastest column-store database implementation on the market today.
What do you dislike?
There are still some glitches in the bulk import COPY process that requires inlining the source query details and the management tool ecosystem is still in the process of maturing.
What problems are you solving with the product? What benefits have you realized?
Vertica's column-store solution essentially makes every column its own index -- while still facilitating row-level selection of data. This makes previously unfeasible requirements -- such as "filter on every column" -- possible even with interactive user interfaces.
Recommendations to others considering the product:
Clearly understand your requirements prior to implementing a column-store database. Queries with complex WHERE clauses and large scale aggregation can complete with lightening speed, new data can be INSERTed with very fast caching, but rapid UPDATEs and DELETEs will need special design considerations (such as chunking, reordering, and/or tombstoning) in a high velocity solution.
Fast and powerful analytics platform
What do you like best?
A lightweight performance-focused implementation and various features:
− IO optimized - it's a columnar store, no indexing structures to maintain like traditional databases, the indexing is achieved by storing the data sorted on disk, which itself is run transparently as a background process;
− Reduced data storage footprint through advanced encoding schemas (RLE, common-delta, etc.) as well as compression algorithms Ability to operate directly on the encoded data;
− Querying will only read specific columns' data, pushing predicates to the storage layer is very important, analytical queries on row store databases will never be able to match that. Columns with RLE are similar to having an infinite number of partitions and also sub-partitioning levels, in some cases if multiple predicates are used with proper sorts it can be incredibly fast.
ANSI SQL compliant, SQL-92 and most of the SQL-99 standard; easy to extend with user-defined functions written in C++, Java, R and to turn it into a powerful data processing engine that is able to easily parallelize, distribute, and partition datasets for processing (moving processing between Hadoop Pig/Hive and Vertica is very simple).
Developer friendly: from verbose explain plans and query profiles to the ability to track execution engine metrics by query paths/operators (e.g. CPU cycles used, rows processed, bytes sent over network, etc.)
Easy to setup and manage fairly large clusters. In our experience a dba should be able to handle many large clusters.
Very stable, easy to scale, reliable, highly available (most of issues we had were hardware issues; never had down time or lost any data).
Constant addition of features, improvements (e.g. support for large data types, GIS package, flex tables, etc.).
− IO optimized - it's a columnar store, no indexing structures to maintain like traditional databases, the indexing is achieved by storing the data sorted on disk, which itself is run transparently as a background process;
− Reduced data storage footprint through advanced encoding schemas (RLE, common-delta, etc.) as well as compression algorithms Ability to operate directly on the encoded data;
− Querying will only read specific columns' data, pushing predicates to the storage layer is very important, analytical queries on row store databases will never be able to match that. Columns with RLE are similar to having an infinite number of partitions and also sub-partitioning levels, in some cases if multiple predicates are used with proper sorts it can be incredibly fast.
ANSI SQL compliant, SQL-92 and most of the SQL-99 standard; easy to extend with user-defined functions written in C++, Java, R and to turn it into a powerful data processing engine that is able to easily parallelize, distribute, and partition datasets for processing (moving processing between Hadoop Pig/Hive and Vertica is very simple).
Developer friendly: from verbose explain plans and query profiles to the ability to track execution engine metrics by query paths/operators (e.g. CPU cycles used, rows processed, bytes sent over network, etc.)
Easy to setup and manage fairly large clusters. In our experience a dba should be able to handle many large clusters.
Very stable, easy to scale, reliable, highly available (most of issues we had were hardware issues; never had down time or lost any data).
Constant addition of features, improvements (e.g. support for large data types, GIS package, flex tables, etc.).
What do you dislike?
Price may be high; small startups trying to keep costs down may choose open source (e.g. HBase, etc.)
There were some stability issues at first when certain errors were bringing down nodes, etc. but have been solved for a while
Supporting large workloads (many concurrent queries) is still not a strength of Vertica.
Loading very large data sets may use some improvements (e.g. in some cases you may have more capacity to parse and segment the data on the client side and stream the data to a specific node thereby directly reducing load and data redistribution between nodes.
Depending on the data model used, in some cases you might have trouble optimizing the queries (large joins with large group-by's on columns across multiple relations);
There were some stability issues at first when certain errors were bringing down nodes, etc. but have been solved for a while
Supporting large workloads (many concurrent queries) is still not a strength of Vertica.
Loading very large data sets may use some improvements (e.g. in some cases you may have more capacity to parse and segment the data on the client side and stream the data to a specific node thereby directly reducing load and data redistribution between nodes.
Depending on the data model used, in some cases you might have trouble optimizing the queries (large joins with large group-by's on columns across multiple relations);
What problems are you solving with the product? What benefits have you realized?
Vertica is not the silver bullet but based on my experience in 9/10 cases in which you need an analytical database, Vertica is probably the answer.
Currently we're using Vertica more as a data processing engine in conjunction with a Hadoop cluster as some of the steps are way more efficient than doing them in Hadoop and easier to manage (e.g. iterative processing steps). We also had a pretty good experience using it with Storm and Hadoop.
The main reasons I usually choose Vertica are it's the performance that’s fairly easy to scale and extend.
Currently we're using Vertica more as a data processing engine in conjunction with a Hadoop cluster as some of the steps are way more efficient than doing them in Hadoop and easier to manage (e.g. iterative processing steps). We also had a pretty good experience using it with Storm and Hadoop.
The main reasons I usually choose Vertica are it's the performance that’s fairly easy to scale and extend.
Recommendations to others considering the product:
Dr. Michael Stonebraker was the co-founder and architect (Vertica is based on the C-Store project). If you haven't heard of him it suffices to know that he received the 2015 Turing Award for his contributions to database systems.
You will need to understand its physical layer and how your queries will access and process the data to come up with the right design (Database Designer can be a great help to get you started) and then you will be amazed how fast you can do data filtering, joins and group bys, etc. on billions of records with a handful of nodes in minutes. At the same time if your queries are suffering from bad segmentation, can't do block processing, push predicates to storage layer, etc. then you will not really get anything from what Vertica has to offer.
At the same time, using Vertica as a traditional OLTP database, with many small transactions inserting/deleting/updating data is not going to take you very far so that’s an obvious case where Vertica is not recommended.
With all the NoSQL, NewSQL buzz I’ve seen there is a misconception that SQL is old, RDBMS don't scale, etc. but the reality is many of these NoSQL products are adding more and more SQL-like features to stay competitive so be sure SQL is here to stay.
You will need to understand its physical layer and how your queries will access and process the data to come up with the right design (Database Designer can be a great help to get you started) and then you will be amazed how fast you can do data filtering, joins and group bys, etc. on billions of records with a handful of nodes in minutes. At the same time if your queries are suffering from bad segmentation, can't do block processing, push predicates to storage layer, etc. then you will not really get anything from what Vertica has to offer.
At the same time, using Vertica as a traditional OLTP database, with many small transactions inserting/deleting/updating data is not going to take you very far so that’s an obvious case where Vertica is not recommended.
With all the NoSQL, NewSQL buzz I’ve seen there is a misconception that SQL is old, RDBMS don't scale, etc. but the reality is many of these NoSQL products are adding more and more SQL-like features to stay competitive so be sure SQL is here to stay.
Efficient Data Ware House for Reporting
What do you like best?
The columnar storage enables to calculate metrics pretty quickly even in billions of rows.
What do you dislike?
The query optimizer is not as great as postgresql and other traditional RDBMS.
It would require external in memory help for speedy data report for external consumers.
It would require external in memory help for speedy data report for external consumers.
What problems are you solving with the product? What benefits have you realized?
Internal and external reporting for ad tech company. The benefit is it abstracts the sharding needs when using relation dbms such as mysql/postgres
Recommendations to others considering the product:
Consider to have more testing when release a major version. the experience was pleasant when we switch from 6.3 to 7
Fantastic Analytics Platform
What do you like best?
Very fast and relatively easy to implement. Very nice SQL implementation (albeit with some minor limitations) with great bulk load facilities. I successfully migrated multiple multi-terabyte customer databases from Netezza to Vertica with huge increases in performance, lower TCO, and reduced administrative overhead.
What do you dislike?
Refreshing lower environments with data from upper environments is easy if the clusters have the same number of nodes, otherwise you have to get more creative. There are out-of-the-box methods to facilitate data refreshes between differing cluster sizes, but it's more of a roll-your-own approach.
Backup and recovery faces the same challenge in that recovering to like cluster sizes is possible, but not so with a target cluster of a different size.
Vertica's SQL implementation is really good, however, there are a number of odd and/or limited implementations for certain things (e.g. NOT IN with NULL returned by subqueries, CTE support but not recursive CTE support, etc). This may be addressed in the latest versions, but these items were present in 7.0.x.
The operating system-level configuration is fairly straightforward, however Vertica is very sensitive to even the slightest misconfiguration. Highly recommend that implementers follow the vendor documentation to the letter when configuring host servers.
Also recommend a high performance, direct-attached storage device for performant backups.
Backup and recovery faces the same challenge in that recovering to like cluster sizes is possible, but not so with a target cluster of a different size.
Vertica's SQL implementation is really good, however, there are a number of odd and/or limited implementations for certain things (e.g. NOT IN with NULL returned by subqueries, CTE support but not recursive CTE support, etc). This may be addressed in the latest versions, but these items were present in 7.0.x.
The operating system-level configuration is fairly straightforward, however Vertica is very sensitive to even the slightest misconfiguration. Highly recommend that implementers follow the vendor documentation to the letter when configuring host servers.
Also recommend a high performance, direct-attached storage device for performant backups.
What problems are you solving with the product? What benefits have you realized?
Legacy DW appliance replacement with lower TCO and improved performance with lots of room to scale.
Recommendations to others considering the product:
Purchase dedicated, direct-attached storage devices for backups (1 per environment), follow vendor configuration instructions to the letter, send your DBA to training, and study up on the SQL limitations.
Great performance
What do you like best?
The ability to use projections to optimize the performance of multiple types of queries on the same data. Live aggregates have also been useful in increasing the efficiency of our database.
What do you dislike?
Getting educated is harder than it should be. My company had a trainer come in, but it would have been nice if there were more self-paced materials.
What problems are you solving with the product? What benefits have you realized?
In terms of query performance, we went from minutes down to sub-second timing. This enabled us to deliver data in new ways, and we have completely changed how our users experience our products.
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