常用格式
textfile
需要定義分隔符，占用空間大，讀寫效率最低，非常容易發生沖突（分隔符）的一種格式，基本上只有需要導入數據的時候才會使用，比如導入csv文件；
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\u0001'
LINES TERMINATED BY '\n'
STORED AS TEXTFILE
json
hive3.0后官方支持json格式，之前需要使用第三方，導入jar，http://www.congiu.net/hive-json-serde/，
add jar hdfs://nn/jarpath/json-udf-1.3.8-jar-with-dependencies.jar;
add jar hdfs://nn/jarpath/json-serde-1.3.8-jar-with-dependencies.jar;
占用空間最大，讀寫效率低，基本上只有需要導入數據的時候才會使用，比如導入json文件；
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
STORED AS TEXTFILE
xml
CREATE TABLE xml_bank(customer_id STRING, income BIGINT, demographics map<string,string>, financial map<string,string>) ROW FORMAT SERDE 'com.ibm.spss.hive.serde2.xml.XmlSerDe' WITH SERDEPROPERTIES ( "column.xpath.customer_id"="/record/@customer_id", "column.xpath.income"="/record/income/text()", "column.xpath.demographics"="/record/demographics/*", "column.xpath.financial"="/record/financial/*" ) TBLPROPERTIES ( "xmlinput.start"="<record customer", "xmlinput.end"="</record>" );
lzo
相比textfile多了lzo壓縮，占用空間更小；
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
STORED AS INPUTFORMAT
'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
orc
列式存儲，占用空間最小，非常適合用來做數倉；
STORED AS ORC
壓縮
STORED AS ORC TBLPROPERTIES ("orc.compression"="ZLIB")
STORED AS ORC TBLPROPERTIES ("orc.compression"="SNAPPY")
注意設置orc壓縮格式前一定要先設置：
set hive.exec.orc.compression.strategy=COMPRESSION;
否則壓縮不生效；
parquet
列式存儲，占用空間居中，如果后期使用spark來處理，parquet是最佳格式；
STORED AS PARQUET
parquet+snappy
STORED AS PARQUET TBLPROPERTIES ("parquet.compression"="SNAPPY")
對比測試
測試表：test_table
測試行數：10億
測試sql類型：aggregation
測試sql：select col_1, count(1) from test_table group by col_1;
測試結果
| fs |
hdfs |
kudu |
|||||
| format |
textfile |
lzo |
parquet |
parquet snappy |
orc |
orc snappy |
|
| capacity |
464.0 G |
169.4 G |
177.2 G |
111.3 G |
71.5 G |
65.7G |
184 G |
|
|
100% |
36% |
37% |
23% |
15% |
14% |
39% |
| Hive2.3.4 |
816 s |
711 s |
250 s |
158 s |
130 s |
127 s |
|
| Hive2.3.4 Tuning |
|
|
251 s |
163 s |
109 s |
96 s |
|
| Hive2.3.4 On spark2.4.0 |
|
|
54 s |
47 s |
149 s |
138 s |
|
| Spark2.1.1 |
371 s |
293 s |
17 s |
16 s |
51 s |
|
|
| Spark2.4.0 |
496 s |
297 s |
16 s |
16 s |
21 s |
21 s |
|
| Drill1.15.0 |
|
|
59 s |
57 s |
75 s |
45 s |
|
| Impala2.12 |
|
|
|
15 s |
|
|
16 s |
| Presto0.215 |
|
|
25 s |
21 s |
13 s |
12 s |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
- 從數據大小和查詢效率上看，表現最好的是presto+orc+snappy；
- hive下最佳格式為orc snappy，數據大小最小，並且查詢最快；
- hive切換engine為spark后，對parquet格式的查詢有一些提升，但是占用相同資源的情況下，遠不如直接使用spark sql快；
- spark2.3以后對orc格式相比之前有很大優化，已經很接近parquet格式；
- impala+parquet+hdfs的性能和impala+kudu差不多，kudu的好處是支持實時更新；
- drill看起來沒有必要；
- spark2.4.0中的parquet為2.4，parquet從2.5開始支持column index，預計以后的spark版本對parquet的查詢會更快；
- impala對orc的支持從3.1開始作為實驗功能的一部分；
詳細數據
yarn 200g 50core
1 hive-2.3.4
set mapreduce.map.memory.mb=4096;
set mapreduce.map.java.opts=-Xmx3072m;
hive-textfile:
Time taken: 816.202 seconds, Fetched: 32 row(s)
Stage-Stage-1: Map: 1831 Reduce: 1009 Cumulative CPU: 27614.77 sec HDFS Read: 498267775168 HDFS Write: 88861 SUCCESS
Total MapReduce CPU Time Spent: 0 days 7 hours 40 minutes 14 seconds 770 msec
hive-lzo:
Time taken: 711.266 seconds, Fetched: 32 row(s)
Stage-Stage-1: Map: 183 Reduce: 711 Cumulative CPU: 13949.24 sec HDFS Read: 181881436157 HDFS Write: 62935 SUCCESS
Total MapReduce CPU Time Spent: 0 days 3 hours 52 minutes 29 seconds 240 msec
hive-orc:
Time taken: 130.194 seconds, Fetched: 32 row(s)
Stage-Stage-1: Map: 275 Reduce: 300 Cumulative CPU: 4368.67 sec HDFS Read: 626004573 HDFS Write: 27178 SUCCESS
Total MapReduce CPU Time Spent: 0 days 1 hours 12 minutes 48 seconds 670 msec
hive-orc snappy:
Time taken: 127.803 seconds, Fetched: 32 row(s)
Stage-Stage-1: Map: 191 Reduce: 276 Cumulative CPU: 4374.74 sec HDFS Read: 580889407 HDFS Write: 25090 SUCCESS
Total MapReduce CPU Time Spent: 0 days 1 hours 12 minutes 54 seconds 740 msec
hive-orc-tuning:
Time taken: 109.539 seconds, Fetched: 32 row(s)
Stage-Stage-1: Map: 275 Reduce: 300 Cumulative CPU: 3051.67 sec HDFS Read: 627064673 HDFS Write: 40321 SUCCESS
Total MapReduce CPU Time Spent: 50 minutes 51 seconds 670 msec
hive-orc snappy-tuning:
Time taken: 94.135 seconds, Fetched: 32 row(s)
Stage-Stage-1: Map: 191 Reduce: 276 Cumulative CPU: 2393.92 sec HDFS Read: 581727151 HDFS Write: 37201 SUCCESS
Total MapReduce CPU Time Spent: 39 minutes 53 seconds 920 msec
hive-parquet:
Time taken: 250.786 seconds, Fetched: 32 row(s)
Stage-Stage-1: Map: 642 Reduce: 744 Cumulative CPU: 10919.85 sec HDFS Read: 873784253 HDFS Write: 65806 SUCCESS
Total MapReduce CPU Time Spent: 0 days 3 hours 1 minutes 59 seconds 850 msec
hive-parquet snappy:
Time taken: 158.009 seconds, Fetched: 32 row(s)
Stage-Stage-1: Map: 367 Reduce: 467 Cumulative CPU: 6246.0 sec HDFS Read: 721915438 HDFS Write: 41707 SUCCESS
Total MapReduce CPU Time Spent: 0 days 1 hours 44 minutes 6 seconds 0 msec
2 hive-2.3.4 on spark-2.4.0
set spark.driver.memory=4g;
set spark.executor.memory=4g;
set spark.executor.instances=10;
hive on spark-parquet:
Time taken: 54.446 seconds, Fetched: 32 row(s)
hive on spark-parquet snappy:
Time taken: 47.364 seconds, Fetched: 32 row(s)
hive on spark-orc:
Time taken: 149.901 seconds, Fetched: 32 row(s)
hive on spark-orc snappy:
Time taken: 138.844 seconds, Fetched: 32 row(s)
3 impala-2.12
MEM_LIMIT=20g * 3
impala-parquet snappy:
Fetched 32 row(s) in 15.10s
+--------------+--------+----------+----------+-------+------------+-----------+---------------+---------------------------------------------------+
| Operator | #Hosts | Avg Time | Max Time | #Rows | Est. #Rows | Peak Mem | Est. Peak Mem | Detail |
+--------------+--------+----------+----------+-------+------------+-----------+---------------+---------------------------------------------------+
| 04:EXCHANGE | 1 | 211.45us | 211.45us | 32 | 50 | 208.00 KB | 0 B | UNPARTITIONED |
| 03:AGGREGATE | 3 | 2.58ms | 2.91ms | 32 | 50 | 34.03 MB | 128.00 MB | FINALIZE |
| 02:EXCHANGE | 3 | 29.23us | 30.92us | 96 | 1.04B | 32.00 KB | 0 B | HASH(cpp_addr_province) |
| 01:AGGREGATE | 3 | 13.29s | 13.97s | 96 | 1.04B | 34.05 MB | 128.00 MB | STREAMING |
| 00:SCAN HDFS | 3 | 723.09ms | 760.01ms | 1.04B | 1.04B | 36.55 MB | 88.00 MB | temp.app_ba_userprofile_prop_nonpolar_view_ext_ps |
+--------------+--------+----------+----------+-------+------------+-----------+---------------+---------------------------------------------------+
impala-kudu:
Fetched 32 row(s) in 15.61s
4 drill-1.15
10g+10g+1g+1g * 3
drill-parquet:
32 rows selected (59.501 seconds)
drill-parquet snappy:
32 rows selected (57.653 seconds)
drill-orc:
32 rows selected (75.749 seconds)
drill-orc snappy:
32 rows selected (45.323 seconds)
5 spark-sql --master yarn --num-executors 10 --executor-memory 4g --driver-memory 4g
5.1 spark-2.1.1
spark sql-textfile:
Time taken: 371.77 seconds, Fetched 32 row(s)
spark sql-lzo:
Time taken: 293.391 seconds, Fetched 32 row(s)
spark sql-parquet:
Time taken: 17.338 seconds, Fetched 32 row(s)
spark sql-parquet snappy:
Time taken: 16.609 seconds, Fetched 32 row(s)
spark sql-orc:
Time taken: 51.959 seconds, Fetched 32 row(s)
5.2 spark-2.4.0
spark sql-textfile:
Time taken: 496.395 seconds, Fetched 32 row(s)
spark sql-lzo:
Time taken: 297.142 seconds, Fetched 32 row(s)
spark sql-parquet:
Time taken: 16.728 seconds, Fetched 32 row(s)
spark sql-parquet snappy:
Time taken: 16.879 seconds, Fetched 32 row(s)
spark sql-orc:
Time taken: 21.432 seconds, Fetched 32 row(s)
spark sql-orc snappy:
Time taken: 21.935 seconds, Fetched 32 row(s)
6 presto
presto-parquet:
Splits: 3,182 total, 3,182 done (100.00%)
0:25 [1.04B rows, 612MB] [42.2M rows/s, 24.9MB/s]
presto-parquet snappy:
Splits: 2,088 total, 2,088 done (100.00%)
0:21 [1.04B rows, 584MB] [49.3M rows/s, 27.8MB/s
presto-orc：
Splits: 1,532 total, 1,532 done (100.00%)
0:13 [1.04B rows, 850MB] [81.7M rows/s, 66.8MB/s]
presto-orc snappy：
Splits: 1,353 total, 1,353 done (100.00%)
0:12 [1.04B rows, 1.13GB] [87.5M rows/s, 97.4MB/s]