HTHou commented on a change in pull request #2601:
URL: https://github.com/apache/iotdb/pull/2601#discussion_r567646365
##########
File path: docs/UserGuide/Comparison/TSDB-Comparison.md
##########
@@ -284,39 +284,58 @@ It is somehow right. But, if you consider the
performance, you may change your m
#### quick review
-Given a workload:
-
* Write:
-10 clients write data concurrently. The number of storage group is 50. There
are 1000 devices and each device has 100 measurements (i.e.,, 100K time series
totally).
-The data type is float and IoTDB uses RLE encoding and Snappy compression.
-IoTDB uses batch insertion API and the batch size is 100 (write 100 data
points per write API call).
+We test write performance from two aspects: *batch size* and *client num*, The
number of storage group is 10. There are 1000 devices and each device has 100
measurements(i.e.,, 100K time series totally).
* Read:
-50 clients read data concurrently. Each client just read data from 1 device
with 10 measurements in one storage group.
+10 clients read data concurrently. The number of storage group is 10. There
are 10 devices and each device has 10 measurements(i.e.,, 100 time series
totally).
+The data type is *double*, encoding type is *GORILLA*
-IoTDB is v0.9.0.
+IoTDB is v0.11.1.
Review comment:
```suggestion
The IoTDB version is v0.11.1.
```
##########
File path: docs/UserGuide/Comparison/TSDB-Comparison.md
##########
@@ -284,39 +284,58 @@ It is somehow right. But, if you consider the
performance, you may change your m
#### quick review
-Given a workload:
-
* Write:
-10 clients write data concurrently. The number of storage group is 50. There
are 1000 devices and each device has 100 measurements (i.e.,, 100K time series
totally).
-The data type is float and IoTDB uses RLE encoding and Snappy compression.
-IoTDB uses batch insertion API and the batch size is 100 (write 100 data
points per write API call).
+We test write performance from two aspects: *batch size* and *client num*, The
number of storage group is 10. There are 1000 devices and each device has 100
measurements(i.e.,, 100K time series totally).
* Read:
-50 clients read data concurrently. Each client just read data from 1 device
with 10 measurements in one storage group.
+10 clients read data concurrently. The number of storage group is 10. There
are 10 devices and each device has 10 measurements(i.e.,, 100 time series
totally).
Review comment:
```suggestion
10 clients read data concurrently. The number of storage group is 10. There
are 10 devices and each device has 10 measurements (i.e.,, 100 time series
total).
```
##########
File path: docs/UserGuide/Comparison/TSDB-Comparison.md
##########
@@ -284,39 +284,58 @@ It is somehow right. But, if you consider the
performance, you may change your m
#### quick review
-Given a workload:
-
* Write:
-10 clients write data concurrently. The number of storage group is 50. There
are 1000 devices and each device has 100 measurements (i.e.,, 100K time series
totally).
-The data type is float and IoTDB uses RLE encoding and Snappy compression.
-IoTDB uses batch insertion API and the batch size is 100 (write 100 data
points per write API call).
+We test write performance from two aspects: *batch size* and *client num*, The
number of storage group is 10. There are 1000 devices and each device has 100
measurements(i.e.,, 100K time series totally).
Review comment:
```suggestion
We test the performance of writing from two aspects: *batch size* and
*client num*. The number of storage group is 10. There are 1000 devices and
each device has 100 measurements(i.e.,, 100K time series total).
```
##########
File path: docs/UserGuide/Comparison/TSDB-Comparison.md
##########
@@ -284,39 +284,58 @@ It is somehow right. But, if you consider the
performance, you may change your m
#### quick review
-Given a workload:
-
* Write:
-10 clients write data concurrently. The number of storage group is 50. There
are 1000 devices and each device has 100 measurements (i.e.,, 100K time series
totally).
-The data type is float and IoTDB uses RLE encoding and Snappy compression.
-IoTDB uses batch insertion API and the batch size is 100 (write 100 data
points per write API call).
+We test write performance from two aspects: *batch size* and *client num*, The
number of storage group is 10. There are 1000 devices and each device has 100
measurements(i.e.,, 100K time series totally).
* Read:
-50 clients read data concurrently. Each client just read data from 1 device
with 10 measurements in one storage group.
+10 clients read data concurrently. The number of storage group is 10. There
are 10 devices and each device has 10 measurements(i.e.,, 100 time series
totally).
+The data type is *double*, encoding type is *GORILLA*
-IoTDB is v0.9.0.
+IoTDB is v0.11.1.
**Write performance**:
-We write 112GB data totally.
+* batch size:
+
+10 clients write data concurrently.
+IoTDB uses batch insertion API and the batch size is distributed from 1(1ms)
to 6000(1min) (write N data points per write API call).
Review comment:
```suggestion
IoTDB uses batch insertion API and the batch size is distributed from 1ms to
1min (write N data points per write API call).
```
##########
File path: docs/UserGuide/Comparison/TSDB-Comparison.md
##########
@@ -327,58 +346,29 @@ We provide a benchmarking tool, called IoTDB-benchamrk
(https://github.com/thula
it supports IoTDB, InfluxDB, KairosDB, TimescaleDB, OpenTSDB. We have a
[article](https://arxiv.org/abs/1901.08304) for comparing these systems using
the benchmark tool.
When we publish the article, IoTDB just entered Apache incubator, so we
deleted the performance of IoTDB in that article. But after comparison, some
results are presented here.
-- **IoTDB: 0.8.0**. (notice: **IoTDB v0.9 outperforms than v0.8**, the result
will be updated once experiments on v0.9 are finished)
-- InfluxDB: 1.5.1.
-- OpenTSDB: 2.3.1 (HBase 1.2.8)
-- KairosDB: 1.2.1 (Cassandra 3.11.3)
-- TimescaleDB: 1.0.0 (PostgreSQL 10.5)
-
All TSDB run on the same server one by one.
- For InfluxDB, we set the cache-max-memory-size and max-series-perbase as
unlimited (otherwise it will be timeout quickly)
Review comment:
```suggestion
- For InfluxDB, we set the cache-max-memory-size and max-series-perbase as
unlimited (otherwise it will be timeout quickly).
```
##########
File path: docs/UserGuide/Comparison/TSDB-Comparison.md
##########
@@ -327,58 +346,29 @@ We provide a benchmarking tool, called IoTDB-benchamrk
(https://github.com/thula
it supports IoTDB, InfluxDB, KairosDB, TimescaleDB, OpenTSDB. We have a
[article](https://arxiv.org/abs/1901.08304) for comparing these systems using
the benchmark tool.
Review comment:
```suggestion
it supports IoTDB, InfluxDB, KairosDB, TimescaleDB, OpenTSDB. We have an
[article](https://arxiv.org/abs/1901.08304) for comparing these systems using
the benchmark tool.
```
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
[email protected]
