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Start spark3.3 + kafka3.2 streaming from scratch
I have not used spark for long days. Today I tried to setup spark to integrate with kafka streaming, but I found the ecosystem has changed a lot.
The main branches for spark and kafka are these:
- spark-3.3.0-bin-hadoop3.tgz (spark 3.3)
- kafka_2.12-3.2.0.tgz (kafka 3.2)
Though scala 2.13 has been released for long days, but both spark and kafka were developed mainly by scala 2.12. When downloading the software, we should choose the version who uses scala 2.12.
Firstly, we want to install scala 2.12 in the system. My system is ubuntu 20.04, x64. Following the steps below to install java ,sdkman, scala and sbt:
$ sudo apt install openjdk-11-jre
$ curl -s "https://get.sdkman.io" | bash
$ sdk install scala 2.12.15
$ sdk install sbtAfter then, download spark and kafka from the links above. Untar the packages, and move them to /opt directory. So in /opt dir I have:
$ ls /opt
kafka sparkThen put these settings in .bash_profile file in user’s home dir:
source "/home/pyh/.sdkman/bin/sdkman-init.sh"
export SPARK_HOME=/opt/spark
export JAVA_HOME=/usr
export PATH=/opt/kafka/bin:/opt/spark/bin:$PATHI have a script “kafka.sh” for managing the kafka service, whose content is as follow:
#!/bin/bash
ACT=$1
TOP=$2
PRE="/opt/kafka"
if [ -z $ACT ];then
echo "$0 action [topic]"
exit
fi
if [ -z $TOP ];then
TOP="quickstart-events"
fi
if [ "$ACT" == "produce" ];then
$PRE/bin/kafka-console-producer.sh --topic $TOP --bootstrap-server localhost:9092
elif [ "$ACT" == "consume" ];then
$PRE/bin/kafka-console-consumer.sh --topic $TOP --from-beginning --bootstrap-server localhost:9092
elif [ "$ACT" == "create" ];then
$PRE/bin/kafka-topics.sh --create --partitions 2 --replication-factor 1 --topic $TOP --bootstrap-server localhost:9092
elif [ "$ACT" == "desc" ];then
$PRE/bin/kafka-topics.sh --describe --topic $TOP --bootstrap-server localhost:9092
elif [ "$ACT" == "startzk" ];then
$PRE/bin/zookeeper-server-start.sh $PRE/config/zookeeper.properties
elif [ "$ACT" == "start" ];then
$PRE/bin/kafka-server-start.sh $PRE/config/server.properties
fiSo, I use this script to start a kafka process and create a topic:
$ kafka.sh startzk # startup zookeeper
$ kafka.sh start # startup kafka
$ kafka.sh create mytest # create a topicThese three commands should be run in three separated terminals. The last step is to create a topic named as “mytest”.
Now, I produce the messages to kafka by a ruby script:
$ cat produce.rb
require 'kafka'
kafka = Kafka.new("localhost:9092", client_id: "ruby-client", resolve_seed_brokers: true)
producer = kafka.producer(required_acks: :all,max_buffer_size: 50_000)
1000.times do
message = rand.to_s
producer.produce(message, key: "key1", topic: "mytest")
end
producer.deliver_messagesTo keep publishing messages continuously, we can do it in bash shell:
$ while [ 1 ];do ruby produce.rb ;sleep 5;doneFor now the messages have been published to kafka successfully. I have to read the streaming from kafka into spark and calculate the messages from within spark.
To setup a scala project:
$ mkdir myproject
$ cd myproject
$ mkdir -p src/main/scala
$ touch build.sbt
$ touch src/main/scala/sparkafka.scalaHere is the content of build.sbt:
name := "sparkafka"
version := "0.1"
scalaVersion := "2.12.15"
libraryDependencies += "org.apache.spark" % "spark-sql-kafka-0-10_2.12" % "3.3.0"
libraryDependencies += "org.apache.spark" %% "spark-sql" % "3.3.0"And the source code in sparkafka.scala:
import org.apache.spark.sql.SparkSession
object Sparkafka {
def main(args:Array[String]):Unit = {
val spark = SparkSession.builder.appName("Mykafka").getOrCreate()
val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("subscribe", "mytest")
.load()
import spark.implicits._
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
val myCount = df.groupBy("key").count()
val query = myCount.writeStream
.outputMode("complete")
.format("console")
.start()
query.awaitTermination()
}
}In myproject directory, I run this command to compile and package the project:
$ sbt package
...
[success] Total time: 4 s, completed Jul 7, 2022, 4:39:19 PMGo to spark’s configuration dir, change the log level in log4j2.properties to error:
$ cat /opt/spark/conf/log4j2.properties |grep error
rootLogger.level = errorThe last step, submit the job to spark. In myproject dir, run the command below:
$ spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.3.0 --class "Sparkafka" --master local[2] target/scala-2.12/sparkafka_2.12-0.1.jarHere is the output in terminal:
-------------------------------------------
Batch: 1
-------------------------------------------
+-------------+-----+
| key|count|
+-------------+-----+
|[6B 65 79 31]| 2000|
+-------------+-----+
-------------------------------------------
Batch: 2
-------------------------------------------
+-------------+-----+
| key|count|
+-------------+-----+
|[6B 65 79 31]| 4000|
+-------------+-----+
-------------------------------------------
Batch: 3
-------------------------------------------
+-------------+-----+
| key|count|
+-------------+-----+
|[6B 65 79 31]| 5000|
+-------------+-----+
-------------------------------------------
Batch: 4
-------------------------------------------
+-------------+-----+
| key|count|
+-------------+-----+
|[6B 65 79 31]| 7000|
+-------------+-----+Finally we got the job run correctly. For production deployment, we read the streaming from kafka, after aggregation we would write the results into a storage such as redis or mysql, instead of writing to terminal as in this sample.
groupMapReduce in Scala, Ruby and Spark
Given the following dataset:
$ head -10 fruits.txt
peach 1
apricot 2
apple 3
haw 1
persimmon 9
orange 2
litchi 9
orange 5
jackfruit 8
crab apple 0We can group and aggregate to count the fruit’s number by their names.
In Scala it’s quite easy because this language has the built-in higher order functions for calculation. The implementation in Scala REPL:
scala> import scala.io.Source
import scala.io.Source
scala> val fd = Source.fromFile("tmp/fruits.txt").getLines().toList
scala> val regex = """(.+)\s+(\d+)""".r
val regex: scala.util.matching.Regex = (.+)\s+(\d+)
scala> fd.map{ case regex(x,y) => (x,y.toInt) }.groupMapReduce(_._1)(_._2)(_+_).toList.sortBy(-_._2).foreach(println)
(blueberry,166)
(cumquat,145)
(lemon,145)
(areca nut,139)
(haw,137)
(banana,134)
(greengage,134)
(raspberry,133)
(longan,132)
(nectarine,128)
(flat peach,125)
(tangerine,122)
(blackberry,121)
(litchi,120)
(watermelon,120)
(peach,117)
(bitter orange,117)
(strawberry,116)
(pawpaw papaya,113)
(persimmon,108)
(orange,104)
(tomato,102)
(mango,102)
(plum,99)
(wax apple,98)
(waxberry red bayberry,95)
(grape,92)
(jackfruit,92)
(pineapple,91)
(pear,89)
(loquat,89)
(coconut,86)
(apple,86)
(pomegranate,84)
(shaddock pomelo,84)
(musk melon,84)
(guava,82)
(honey peach,82)
(apricot,81)
(starfruit,80)
(sugar cane,79)
(crab apple,75)
(cherry,73)Ruby also implements this job well by using its Array methods. Though Ruby has no built-in groupMapReduce method, but I have implemented one for which you can get from this repo. The code below is run in Ruby’s interactive shell (irb).
irb(main):001:0> require './bitfox'
=> true
irb(main):002:0> file = File.readlines('tmp/fruits.txt')
irb(main):011:-> file.map {|s| (x,y) = s.chomp.split("\t"); [x,y.to_i] }.reduceByKey {|x,y| x+y}.sort_by{ |s| -s[1] }
=>
[["blueberry", 166],
["cumquat", 145],
["lemon", 145],
["areca nut", 139],
["haw", 137],
["greengage", 134],
["banana", 134],
["raspberry", 133],
["longan", 132],
["nectarine", 128],
["flat peach", 125],
["tangerine", 122],
["blackberry", 121],
["litchi", 120],
["watermelon", 120],
["peach", 117],
["bitter orange", 117],
["strawberry", 116],
["pawpaw papaya", 113],
["persimmon", 108],
["orange", 104],
["tomato", 102],
["mango", 102],
["plum", 99],
["wax apple", 98],
["waxberry red bayberry", 95],
["jackfruit", 92],
["grape", 92],
["pineapple", 91],
["pear", 89],
["loquat", 89],
["apple", 86],
["coconut", 86],
["pomegranate", 84],
["shaddock pomelo", 84],
["musk melon", 84],
["honey peach", 82],
["guava", 82],
["apricot", 81],
["starfruit", 80],
["sugar cane", 79],
["crab apple", 75],
["cherry", 73]]In my implementation above, Ruby uses the method reduceByKey instead of groupMapReduce in Scala. That’s to say, Ruby and Spark use the method name “reduceBykey”, while Scala uses the name “groupMapReduce”. All three methods have the same effect.
The last is Spark’s implementation. It’s quite simple to run this statistics in spark-shell.
scala> val rdd = sc.textFile("tmp/fruits.txt")
rdd: org.apache.spark.rdd.RDD[String] = tmp/fruits.txt MapPartitionsRDD[1] at textFile at <console>:23
scala> val regex = """(.+)\s+(\d+)""".r
regex: scala.util.matching.Regex = (.+)\s+(\d+)
scala> rdd.map{ case regex(x,y) => (x,y.toInt) }.reduceByKey( _+_ ).sortBy(-_._2).foreach(println)
(blueberry,166)
(cumquat,145)
(lemon,145)
(areca nut,139)
(haw,137)
(banana,134)
(greengage,134)
(raspberry,133)
(longan,132)
(nectarine,128)
(flat peach,125)
(tangerine,122)
(blackberry,121)
(watermelon,120)
(litchi,120)
(bitter orange,117)
(peach,117)
(strawberry,116)
(pawpaw papaya,113)
(persimmon,108)
(orange,104)
(tomato,102)
(mango,102)
(plum,99)
(wax apple,98)
(waxberry red bayberry,95)
(grape,92)
(jackfruit,92)
(pineapple,91)
(pear,89)
(loquat,89)
(apple,86)
(coconut,86)
(musk melon,84)
(shaddock pomelo,84)
(pomegranate,84)
(guava,82)
(honey peach,82)
(apricot,81)
(starfruit,80)
(sugar cane,79)
(crab apple,75)
(cherry,73)It’s not hard to understand how groupMapReduce is implemented. If you have checked my code in Github, you will find the code is little.
def reduceByKey
if block_given?
group_by {|x| x[0]}.map {|x,y| y.reduce {|x,y| [ x[0], yield(x[1],y[1]) ]}}
else
raise "no block given"
end
endPerl and Python can implement this method as well. But they are not that convenient. For data programming, Scala and Spark are really the best. If no considering the performance, Ruby is fun too.
Time difference on handling the file larger than memory
Apache Spark can deal with big file well even if file size is much larger than the memory.
For instance, I have the file “rate.csv” which is 3.2GB in size:
$ du -h rate.csv
3.2G rate.csvBut my VPS has only 2GB total memory:
$ free -m
total used free shared buff/cache available
Mem: 1992 1652 206 4 134 197
Swap: 1023 521 502Even though Spark can read and handle this file well. For example, I load the file into spark and issue a SQL query via its Dataframe API.
// define the schema
scala> val schema="uid STRING,item STRING,rate FLOAT,time INT"
val schema: String = uid STRING,item STRING,rate FLOAT,time INT
// load csv file into spark as a dataframe
scala> val df = spark.read.format("csv").schema(schema).load("skydrive/rate.csv")
val df: org.apache.spark.sql.DataFrame = [uid: string, item: string ... 2 more fields]
// print the schema
scala> df.printSchema()
root
|-- uid: string (nullable = true)
|-- item: string (nullable = true)
|-- rate: float (nullable = true)
|-- time: integer (nullable = true)
// get the items count
scala> df.count()
val res1: Long = 82677131
// get the partitions number
scala> df.rdd.getNumPartitions
val res3: Int = 26
// issue a query by grouping item and aggregating rate
scala> df.groupBy("item").agg(avg("rate").alias("avg_rate")).orderBy(desc("avg_rate")).show()
+----------+--------+
| item|avg_rate|
+----------+--------+
|0001061100| 5.0|
|0001543849| 5.0|
|0001061127| 5.0|
|0001019880| 5.0|
|0001062395| 5.0|
|0000143502| 5.0|
|000014357X| 5.0|
|0001527665| 5.0|
|000107461X| 5.0|
|0000191639| 5.0|
|0001127748| 5.0|
|0000791156| 5.0|
|0001203088| 5.0|
|0001053744| 5.0|
|0001360183| 5.0|
|0001042335| 5.0|
|0001374400| 5.0|
|0001046810| 5.0|
|0001380877| 5.0|
|0001050230| 5.0|
+----------+--------+
only showing top 20 rowsAs you see above, the query run pretty well. Spark didn’t throw out any memory overflow error. The total query time is: 1 min 46 seconds.
Apache Drill is quite memory sensitive, even so it can make the job done under this limited memory. For example:
> select columns[1] as `item`, avg(cast(columns[2] as Float)) as `avg_rate`
> from `rate.csv` group by `item` order by `avg_rate` desc limit 20;
+------------+----------+
| item | avg_rate |
+------------+----------+
| 0004133900 | 5.0 |
| 0005019222 | 5.0 |
| 0002557991 | 5.0 |
| 0002214830 | 5.0 |
| 0002720213 | 5.0 |
| 0002326817 | 5.0 |
| 0002254352 | 5.0 |
| 000225316X | 5.0 |
| 0002199203 | 5.0 |
| 0001712705 | 5.0 |
| 0002617501 | 5.0 |
| 0002113848 | 5.0 |
| 0001840266 | 5.0 |
| 0002117576 | 5.0 |
| 0001360183 | 5.0 |
| 0000202010 | 5.0 |
| 0002179083 | 5.0 |
| 0001954792 | 5.0 |
| 0002005468 | 5.0 |
| 0005097231 | 5.0 |
+------------+----------+
20 rows selected (119.311 seconds)As you see Drill run well on this scenario too. The total query time is: 1 min 59 seconds.
Then I gave Mysql a try. I know for limited memory Mysql handles this scale of data very slow. Though the VM has only 2GB memory, I had to adjust two important memory arguments for Mysql.
mysql> show variables like 'innodb_buffer_pool_size';
+-------------------------+-----------+
| Variable_name | Value |
+-------------------------+-----------+
| innodb_buffer_pool_size | 402653184 |
+-------------------------+-----------+
1 row in set (0.01 sec)
mysql> show variables like 'key_buffer_size';
+-----------------+-----------+
| Variable_name | Value |
+-----------------+-----------+
| key_buffer_size | 134217728 |
+-----------------+-----------+
1 row in set (0.02 sec)The tuning to Mysql includes ‘innodb_buffer_pool_size’ was set to 384MB, ‘key_buffer_size’ was set to 128MB.
After then I created the table, and loaded the data into this table by using the statements below.
mysql> create table rate(userId varchar(24), itemId varchar(24),rating float, time int);
Query OK, 0 rows affected (0.11 sec)
mysql> load data local infile 'skydrive/rate.csv' into table rate FIELDS TERMINATED BY ',';
Query OK, 82677131 rows affected (21 min 21.25 sec)
Records: 82677131 Deleted: 0 Skipped: 0 Warnings: 0Loading the data takes time 21 min 21 sec. And I need to add an index to the queried column, in this case it’s itemId.
mysql> create index itemIndex on rate(itemId);
Query OK, 0 rows affected (9 min 22.60 sec)
Records: 0 Duplicates: 0 Warnings: 0Finally I made the query in Mysql:
mysql> select itemId, avg(rating) as avg_rate from rate group by itemId order by avg_rate desc limit 20;
+------------+----------+
| itemId | avg_rate |
+------------+----------+
| 0000191639 | 5 |
| 0000143529 | 5 |
| 0000143502 | 5 |
| 0000202010 | 5 |
| 0000053155 | 5 |
| 0001019880 | 5 |
| 0001018043 | 5 |
| 000014357X | 5 |
| 000077135X | 5 |
| 0001026038 | 5 |
| 0000401048 | 5 |
| 0000000078 | 5 |
| 0000230022 | 5 |
| 0000913154 | 5 |
| 0000143588 | 5 |
| 0000466603 | 5 |
| 0001024388 | 5 |
| 0001006657 | 5 |
| 0000791156 | 5 |
| 0000174076 | 5 |
+------------+----------+
20 rows in set (16 min 29.75 sec)Mysql’s total query time is: 16 min 30 seconds.
For comparison to Mysql, I installed Postgresql in this VM. It’s the default installation from Ubuntu apt source, with the basic tuning as below.
postgres=# show shared_buffers;
shared_buffers
----------------
384MB
(1 row)
postgres=# show work_mem;
work_mem
----------
16MB
(1 row)I created the same table as in Mysql and setup the index to the queried column.
bigdata=# \d rate
Table "public.rate"
Column | Type | Collation | Nullable | Default
--------+-----------------------+-----------+----------+---------
userid | character varying(24) | | |
itemid | character varying(24) | | |
rating | double precision | | |
time | integer | | |
Indexes:
"itemindex" btree (itemid)Then I loaded the data from csv to the table:
bigdata=# \copy rate FROM '/home/pyh/skydrive/rate.csv' DELIMITER ',' CSV;
COPY 82677131And the same query was issued to Postgresql. As you see the command top’s output following, Postgresql uses 3 threads in the progress of shuffling. This consumes out all the available memory.
0945 postgres 20 0 1311264 640036 3336 D 20.9 31.4 0:20.64 postgres
30944 postgres 20 0 1335840 637304 3356 D 18.6 31.2 0:20.86 postgres
30330 postgres 20 0 1383316 558332 3272 R 49.3 27.4 4:45.60 postgres But finally Postgresql finished the job as well:
bigdata=# select itemId, avg(rating) as avg_rate from rate group by itemId order by avg_rate desc limit 20;
itemid | avg_rate
------------+----------
0001006657 | 5
0001024388 | 5
0000913154 | 5
0000791156 | 5
0001019880 | 5
0000466603 | 5
000077135X | 5
0000191639 | 5
0000174076 | 5
000014357X | 5
0001018043 | 5
0000202010 | 5
0000143529 | 5
0000230022 | 5
0000000078 | 5
0000401048 | 5
0000053155 | 5
0000143588 | 5
0000143502 | 5
0001026038 | 5
(20 rows)
Time: 156100.056 ms (02:36.100)The total query time in Postgresql is: 2 min 36 seconds.
So, for this limited memory and this scale of dataset, the specific big data apps have much more efficiency than the traditional Mysql. Though Postgresql is one of the RDBMs, it’s much faster than Mysql too. Because it uses multi-threading for the shuffling progress, while the drawback is it consumes the most memory than all the three others.
All queries above were run for several times, the minimum values were taken as the final results.
The query time comparison:
| Data App | Spark | Drill | Mysql | Postgresql |
| Query Time | 106 sec | 119 sec | 990 sec | 156 sec |
Here are the App’s versions:
// Mysql version
mysql> select version();
+-------------------------+
| version() |
+-------------------------+
| 5.7.37-0ubuntu0.18.04.1 |
+-------------------------+
1 row in set (0.01 sec)
// Postgresql version
bigdata=# select version();
version
--------------------------------------------------------------------------------------------------------------------------------------
PostgreSQL 10.19 (Ubuntu 10.19-0ubuntu0.18.04.1) on x86_64-pc-linux-gnu, compiled by gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0, 64-bit
(1 row)
// Drill version
apache drill (dfs.skydrive)> select version from sys.version;
+---------+
| version |
+---------+
| 1.20.0 |
+---------+
1 row selected (0.641 seconds)
// Spark version
version 3.2.1
Using Scala version 2.13.5 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_321)The VM I was using for this test has 2G dedicated memory, 2 Intel cores, 50G NVME disk. It’s good to see all Apps run well under this limited memory. But Mysql is really slow comparing to the others.
A simple comparison for three SQL engines
I have a data from our production environment. The data is just words list, which contains 11922695 items.
I load the data into Mysql, Spark and Drill for a simple test against their query performance.
The test environment:
- OS: Ubuntu 18.04 Linux x86_64, KVM instance
- Hardware specs: 2 AMD cores, 4GB memory, 40GB NVME disk
- Java: Openjdk version 11.0.13
- Mysql: version 5.7.36, default installation, only one change by setting key_buffer_size to 256MB
- Spark: version 3.2.0, deployed as local mode
- Drill: version 1.20, run as drill-embedded
I load the words into the above databases first.
In spark:
scala> val list = sc.textFile("words.txt").toDF("word")
list: org.apache.spark.sql.DataFrame = [word: string]In mysql:
mysql> create table words (word varchar(32));
Query OK, 0 rows affected (0.01 sec)
mysql> load data local infile './words.txt' into table words;
Query OK, 11922695 rows affected (29.88 sec)In drill I need to rename the file to a csv, then query it directly like:
apache drill (dfs.pyh)> select * from `words.csv` limit 2;
+------+
| WORD |
+------+
| on |
| jan |
+------+
2 rows selected (0.235 seconds)Hence I can make a simple SQL query to the three data objects. The purpose is to group the words and count their numbers.
First query in spark:
scala> list.groupBy("word").count.orderBy(desc("count")).show(20)
+----+------+
|word| count|
+----+------+
| the|597563|
| to|466569|
| a|228042|
| is|223282|
| and|215099|
| in|176847|
| i|174579|
| for|165258|
| on|164483|
| of|161266|
|this|159244|
| you|128854|
|that|126949|
| at|126562|
| it|117224|
| be| 89777|
|from| 87112|
|with| 86998|
| not| 85245|
| if| 82798|
+----+------+
only showing top 20 rowsSpark doesn’t show its query time. I got the time roughly by iOS’s Stopwatch app. I have run above queries some times, the average query time is about 3.7 second.
Second query in mysql:
mysql> select word,count(*) as dd from words group by word order by dd desc limit 20;
+------+--------+
| word | dd |
+------+--------+
| the | 597563 |
| to | 466569 |
| a | 228042 |
| is | 223282 |
| and | 215099 |
| in | 176847 |
| i | 174579 |
| for | 165258 |
| on | 164483 |
| of | 161266 |
| this | 159244 |
| you | 128854 |
| that | 126949 |
| at | 126562 |
| it | 117224 |
| be | 89777 |
| from | 87112 |
| with | 86998 |
| not | 85245 |
| if | 82798 |
+------+--------+
20 rows in set (11.05 sec)Then I created an index for this mysql table and query again:
mysql> create index wordIndex on words(word);
Query OK, 0 rows affected (40.21 sec)
Records: 0 Duplicates: 0 Warnings: 0
mysql> select word,count(*) as dd from words group by word order by dd desc limit 20;
+------+--------+
| word | dd |
+------+--------+
| the | 597563 |
| to | 466569 |
| a | 228042 |
| is | 223282 |
| and | 215099 |
| in | 176847 |
| i | 174579 |
| for | 165258 |
| on | 164483 |
| of | 161266 |
| this | 159244 |
| you | 128854 |
| that | 126949 |
| at | 126562 |
| it | 117224 |
| be | 89777 |
| from | 87112 |
| with | 86998 |
| not | 85245 |
| if | 82798 |
+------+--------+
20 rows in set (5.10 sec)As you see mysql has huge performance improvement after adding the index.
The last query in drill:
apache drill (dfs.pyh)> select word,count(*) as dd from `words.csv` group by word order by dd desc limit 20;
+------+--------+
| word | dd |
+------+--------+
| the | 597563 |
| to | 466569 |
| a | 228042 |
| is | 223282 |
| and | 215099 |
| in | 176847 |
| i | 174579 |
| for | 165258 |
| on | 164483 |
| of | 161266 |
| this | 159244 |
| you | 128854 |
| that | 126949 |
| at | 126562 |
| it | 117224 |
| be | 89777 |
| from | 87112 |
| with | 86998 |
| not | 85245 |
| if | 82798 |
+------+--------+
20 rows selected (3.507 seconds)I have run the above queries some times, choose an average running time 3.5 second.
So the comparison of query time in three applications:
| Spark | Mysql | Drill | |
| Query time | 3.7s | with index: 5.1s without index: 11s | 3.5s |
It’s not surprised that for big data Mysql is not as efficient as the other two. Spark and Drill have the similar performance in this scenario. While Drill is much easier to use as you don’t have to know Scala programming.