All: I am looking for someone with more knowledge to check my understanding of Hive and Spark
I have been researching different large scale database solutions and I am trying to understand the difference in execution between Hive and Spark. I attempted to install Hadoop, Hive, and Spark to see how they perform. I was able to get Hadoop and Spark to work. I was unable to get Hive to work.
When I ran queries in Spark after they passed through the optimizer, it seems that the biggest advantage is that only the relevant table data is selected from the source at the earliest inception. So if I only needed Table1.columns(A,B,C) in the final answer, but told the system to JOIN Table1 & Table2 on (Table1.A=Table2.B) it immediately reduces the carried table to only the relevant items...I do not think Hive performs that way. I believe it will do the full join and perform the reduction later.
There are also differences in the memory storage (Hive going back the the HDFS frequently, vs Spark keeping things in RAM). This has both advantages and disadvantages depending on the data set/query.
Unfortunately because I cannot get Hive to run, my theory is based off of reading outputs of other people running things in Hive.
I Think hive and spark originally have different goals, and their execution styles are based on those goals.
Apache spark is a framework that allows you to do calculations on big datasets. stored on hdfs
Hive is an SQL interface to retriev data stored in an hdfs, and other clusterized and object store filesystems (S3 is an example) in a structured way.
Spark keeps things on ram because its more focused on making calculations with the data sets. Hive is more focused on retrieving data in a structured way, so it does not focus on speed that much (that being said, there have been improvements in hive, like llap that are meant to improve performance).
I like to use analogies with traditional software tools. On one side, you can have a relational database, and on the other side, a programming language. They both overlap in some functionality (you can write and read to disk with the programming language, and you can do some calculations with the sql engine. However, if the task at hand requires intensive and complex calculations you would probably use the programming language. If you are looking for a system that lets you store data in a structured way, you would go for the sql engine.
Hive on Tez and Spark both use Ram(memory) for operating on data . The number of partitions computed which will be treated as individual tasks would be quite different from Hive on Tez vs Spark . Hive on Tez by default tries to use combiner to merge certain splits into single partition . Hive one Tez seem to handle autoscaling of clusters in a better way than spark and does work most of the time.Spark doesn't work with autoscaling it would have lot of shuffle errors and will fail when there are multiple stages . But given a fixed size of cluster Spark seems to perform better over Hive on TEZ this could be attributed to some of the optimizations done and also how the shuffle ,serialization etc are implemented .
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I'm interested only in query performance reasons and architectural differences behind them. All answers I've seen before were outdated or hadn't provide me with enough context of WHY Impala is better for ad hoc queries.
From 3 considerations below only the 2nd point explain why Impala is faster on bigger datasets. Could you please contribute to the following statements?
Impala doesn't miss time for query pre-initialization, means impalad daemons are always running & ready. In other hand, Spark Job Server provide persistent context for the same purposes.
Impala is in-memory and can spill data on disk, with performance penalty, when data doesn't have enough RAM. The same is true for Spark. The main difference is that Spark is written on Scala and have JVM limitations, so workers bigger than 32 GB aren't recommended (because of GC). In turn, [wrong, see UPD] Impala is implemented on C++, and has high hardware requirements: 128-256+ GBs of RAM recommended. This is very significant, but should benefit Impala only on datasets that requires 32-64+ GBs of RAM.
Impala is integrated with Hadoop infrastructure. AFAIK the main reason to use Impala over another in-memory DWHs is the ability to run over Hadoop data formats without exporting data from Hadoop. Means Impala usually use the same storage/data/partitioning/bucketing as Spark can use, and do not achieve any extra benefit from data structure comparing to Spark. Am I right?
P.S. Is Impala faster than Spark in 2019? Have you seen any performance benchmarks?
UPD:
Questions update:
I. Why Impala recommends 128+ GBs RAM? What is an implementation language of each Impala's component? Docs say that "Impala daemons run on every node in the cluster, and each daemon is capable of acting as the query planner, the query coordinator, and a query execution engine.". If impalad is Java, than what parts are written on C++? Is there smth between impalad & columnar data? Are 256 GBs RAM required for impalad or some other component?
II. Impala loose all in-memory performance benefits when it comes to cluster shuffles (JOINs), right? Does Impala have any mechanics to boost JOIN performance compared to Spark?
III. Impala use Multi-Level Service Tree (smth like Dremel Engine see "Execution model" here) vs Spark's Directed Acyclic Graph. What does actually MLST vs DAG mean in terms of ad hoc query performance? Or it's a better fit for multi-user environment?
First off, I don't think comparison of a general purpose distributed computing framework and distributed DBMS (SQL engine) has much meaning. But if we would still like to compare a single query execution in single-user mode (?!), then the biggest difference IMO would be what you've already mentioned -- Impala query coordinators have everything (table metadata from Hive MetaStore + block locations from NameNode) cached in memory, while Spark will need time to extract this data in order to perform query planning.
Second biggie would probably be shuffle implementation, with Spark writing temp files to disk at stage boundaries against Impala trying to keep everything in-memory. Leading to a radical difference in resilience - while Spark can recover from losing an executor and move on by recomputing missing blocks, Impala will fail the entire query after a single impalad daemon crash.
Less significant performance-wise (since it typically takes much less time compared to everything else) but architecturally important is work distribution mechanism -- compiled whole stage codegens sent to the workers in Spark vs. declarative query fragments communicated to daemons in Impala.
As far as specific query optimization techniques (query vectorization, dynamic partition pruning, cost-based optimization) -- they could be on par today or will be in the near future.
I am a newbie in Spark SQL world. I am currently migrating my application's Ingestion code which includes ingesting data in stage,Raw and Application layer in HDFS and doing CDC(change data capture), this is currently written in Hive queries and is executed via Oozie. This needs to migrate into a Spark application(current version 1.6). The other section of code will migrate later on.
In spark-SQL, I can create dataframes directly from tables in Hive and simply execute queries as it is (like sqlContext.sql("my hive hql") ). The other way would be to use dataframe APIs and rewrite the hql in that way.
What is the difference in these two approaches?
Is there any performance gain with using Dataframe APIs?
Some people suggested, there is an extra layer of SQL that spark core engine has to go through when using "SQL" queries directly which may impact performance to some extent but I didn't find any material substantiating that statement. I know the code would be much more compact with Datafrmae APIs but when I have my hql queries all handy would it really worth to write complete code into Dataframe API?
Thank You.
Question : What is the difference in these two approaches?
Is there any performance gain with using Dataframe APIs?
Answer :
There is comparative study done by horton works. source...
Gist is based on situation/scenario each one is right. there is no
hard and fast rule to decide this. pls go through below..
RDDs, DataFrames, and SparkSQL (infact 3 approaches not just 2):
At its core, Spark operates on the concept of Resilient Distributed Datasets, or RDD’s:
Resilient - if data in memory is lost, it can be recreated
Distributed - immutable distributed collection of objects in memory partitioned across many data nodes in a cluster
Dataset - initial data can from from files, be created programmatically, from data in memory, or from another RDD
DataFrames API is a data abstraction framework that organizes your data into named columns:
Create a schema for the data
Conceptually equivalent to a table in a relational database
Can be constructed from many sources including structured data files, tables in Hive, external databases, or existing RDDs
Provides a relational view of the data for easy SQL like data manipulations and aggregations
Under the hood, it is an RDD of Row’s
SparkSQL is a Spark module for structured data processing. You can interact with SparkSQL through:
SQL
DataFrames API
Datasets API
Test results:
RDD’s outperformed DataFrames and SparkSQL for certain types of data processing
DataFrames and SparkSQL performed almost about the same, although with analysis involving aggregation and sorting SparkSQL had a slight advantage
Syntactically speaking, DataFrames and SparkSQL are much more intuitive than using RDD’s
Took the best out of 3 for each test
Times were consistent and not much variation between tests
Jobs were run individually with no other jobs running
Random lookup against 1 order ID from 9 Million unique order ID's
GROUP all the different products with their total COUNTS and SORT DESCENDING by product name
In your Spark SQL string queries, you won't know a syntax error until runtime (which could be costly), whereas in DataFrames syntax errors can be caught at compile time.
Couple more additions. Dataframe uses tungsten memory representation , catalyst optimizer used by sql as well as dataframe. With Dataset API, you have more control on the actual execution plan than with SparkSQL
If query is lengthy, then efficient writing & running query, shall not be possible.
On the other hand, DataFrame, along with Column API helps developer to write compact code, which is ideal for ETL applications.
Also, all operations (e.g. greater than, less than, select, where etc.).... ran using "DataFrame" builds an "Abstract Syntax Tree(AST)", which is then passed to "Catalyst" for further optimizations. (Source: Spark SQL Whitepaper, Section#3.3)
I want to save and read the data from HBase from/to Spark.
I want to get the Dataframe abstraction as dataframe is best for memory management compared to RDD and it is convenient to do any processing.
I looked at possible candidates for getting Dataframe abstraction. One of them is Phoenix based solution. I do not want to have pheonix layer on top of HBase due to approvals. I searched for other solutions, but would want to know the best possibility that someone had tried.
We have a performant one at Splice Machine (Open Source). We wrote a separate InputFormat for HBase so we can read directly from store files in hbase vs. performing remote scans. The killer for Spark performance on top of hbase is the remote scan based InputFormat (i.e. how you read the data).
Sean Busbey at Cloudera has worked on a Spark HBase connector and here is a blog from HortonWorks on a similar idea...
http://hortonworks.com/blog/spark-hbase-dataframe-based-hbase-connector/
The "connectors" functionally work but perform poorly for large data sets.
Hope this helps and good luck.
While fetching and manipulating data from HBASE using spark, *Spark sql join* vs *spark dataframe join* - which one is faster?
RDD always Outperform Dataframe and SparkSQL, but from my experience Dataframe perform well compared to SparkSQL. Dataframe function perform well compare to spark sql.Below link will give some insights on this.
Spark RDDs vs DataFrames vs SparkSQL
As far as I can tell， they should behave the same regarding to performance. SQL internally will work as DataFrame
I don't have access to a cluster to properly test but I imagine that the Spark SQL just compiles down to the native data frame code.
The rule of thumb I've heard is that the SQL code should be used for exploration and dataframe operations for production code.
Spark SQL brings a powerful new optimization framework called Catalyst. Using Catalyst, Spark can automatically transform SQL queries so that they execute more efficiently.
A DataFrame is a Dataset organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations, that provides the benefits of RDDs (strong typing, ability to use powerful lambda functions) with the benefits of Spark SQL’s optimized execution engine.
The execution speed will be the same, because they use same optimization algorithms.
If the join might be shared across queries carefully implemented join with RDDs might be a good option. However if this is not the case let spark/catalyst do it's job and join within spark sql. It will do all the optimization. So you wouldn't have to maintain your join logic etc.
Spark SQL join and Spark Dataframe join are almost same thing. The join is actually delegated to RDD operations under the hood. On top of RDD operation we have convenience methods like spark sql, data frame or data set. In case of spark sql it needs to spend a tiny amount of extra time to parse the SQL.
It should be evaluated more in terms of good programming practice. I like dataset because you can catch syntax errors while compiling. And the encodes behind the scene takes care of compacting the data and executing the query.
I did some performance analysis for sql vs dataframe on Cassandra using spark, I think it will be the same for HBASE also.
According to me sql works faster than dataframe approach. The reason behind this might be that in the dataframe approach there are lot of java object's involved. In sql approach everything is done in-memory.
Attaching results.
I would like to hear your thoughts and experiences on the usage of CQL and in-memory query engine Spark/Shark. From what I know, CQL processor is running inside Cassandra JVM on each node. Shark/Spark query processor attached with a Cassandra cluster is running outside in a separated cluster. Also, Datastax has DSE version of Cassandra which allows to deploy Hadoop/Hive. The question is in which use case we would pick a specific solution instead of the other.
I will share a few thoughts based on my experience. But, if possible for you, please let us know about your use-case. It'll help us in answering your queries in a better manner.
1- If you are going to have more writes than reads, Cassandra is obviously a good choice. Having said that, if you are coming from SQL background and planning to use Cassandra then you'll definitely find CQL very helpful. But if you need to perform operations like JOIN and GROUP BY, even though CQL solves primitive GROUP BY use cases through write time and compact time sorts and implements one-to-many relationships, CQL is not the answer.
2- Spark SQL (Formerly Shark) is very fast for the two reasons, in-memory processing and planning data pipelines. In-memory processing makes it ~100x faster than Hive. Like Hive, Spark SQL handles larger than memory data types very well and up to 10x faster thanks to planned pipelines. Situation shifts to Spark SQL benefit when multiple data pipelines like filter and groupBy are present. Go for it when you need ad-hoc real time querying. Not suitable when you need long running jobs over gigantic amounts of data.
3- Hive is basically a warehouse that runs on top of your existing Hadoop cluster and provides you SQL like interface to handle your data. But Hive is not suitable for real-time needs. It is best suited for offline batch processing. Doesn't need any additional infra as it uses underlying HDFS for data storage. Go for it when you have to perform operations like JOIN, GROUP BY etc on large dataset and for OLAP.
Note : Spark SQL emulates Apache Hive behavior on top of Spark, so it supports virtually all Hive features but potentially faster. It supports the existing Hive Query language, Hive data formats (SerDes), user-defined functions (UDFs), and queries that call external scripts.
But I think you will be able to evaluate the pros and cons of all these tools properly only after getting your hands dirty. I could just suggest based on your questions.
Hope this answers some of your queries.
P.S. : The above answer is based on solely my experience. Comments/corrections are welcome.
There is a very good effort for benchmark documented here - https://amplab.cs.berkeley.edu/benchmark/