目前我们日志收集组件使用的是filebeat6.6.1，在某业务上线以后，发生了日志收集延迟的问题，最差的情况，延迟两天以上。严重影响了下游数据分析项目。
分析该业务日志之后，发现该业务日志量大，但是单日志filed非常少。
之前我们在压测的时候，已经设置了output批量发送。再加上观察kafka集群的性能监控，基本上可以排查是下游集群的影响。
针对该问题，今天的主角该出场了--pprof。
pprof
PProf 工具是自带的我们检测 Golang 开发应用性能的利器。Golang 提供的两个官方包 runtime/pprof， net/http/pprof 能方便的采集程序运行的堆栈、goroutine、内存分配和占用、io 等信息的 .prof 文件，然后可以使用 go tool pprof 分析 .prof 文件。两个包的作用是一样的，只是使用方式的差异。
net/http/pprof 其实就是对runtime/pprof的封装，用于webserver。今天我们主要使用runtime/pprof。
debug过程
1 开启filebeat pprof
默认filebeat 的pprof 是关闭的。开启的方法如下：
./filebeat --c /etc/filebeat.yml --path.data /usr/share/filebeat/data --path.logs /var/log/filebeat --httpprof 0.0.0.0:60602 查看30scpu信息
go tool pprof http://0.0.0.0:6060/debug/pprof/profile30s后，我们输入top10命令,有如下打印信息：
Showing top 10 nodes out of 197
flat flat% sum% cum cum%
21.45s 13.42% 13.42% 70.09s 43.85% runtime.gcDrain
15.49s 9.69% 23.11% 39.83s 24.92% runtime.scanobject
11.38s 7.12% 30.23% 11.38s 7.12% runtime.futex
7.86s 4.92% 35.15% 16.30s 10.20% runtime.greyobject
7.82s 4.89% 40.04% 7.82s 4.89% runtime.markBits.isMarked (inline)
5.59s 3.50% 43.53% 5.59s 3.50% runtime.(*lfstack).pop
5.51s 3.45% 46.98% 6.05s 3.78% runtime.heapBitsForObject
5.26s 3.29% 50.27% 13.92s 8.71% runtime.sweepone
4.04s 2.53% 52.80% 4.04s 2.53% runtime.memclrNoHeapPointers
3.37s 2.11% 54.91% 4.40s 2.75% runtime.runqgrab发现太多的cpu时间浪费在GC上，基本上可以肯定filebeat在小日志场景下，创建了大量的对象。此时大家应该都想到了sync.pool。
我们需要更详细的信息，需要查看具体的调用关系，发现那里在大量的创建对象。
输入 web命令，将会看到如下的图，以图形化的方式展示了GC的占用：
通过调用关系找到了newobject大量调用：
接着找到了根源：
可以看出根源在sarama 库，filebeat 通过sarama 来将message 写到kafka中。主要是encode方法（flate NewWriter）。我们都知道该方法是用来压缩的，我们的filebeat 默认是采用了gzip压缩。
所以接下来我们需要通过代码验证一下猜想了。下面通过heap图侧面证明之前的猜想。
3 旧代码
func (m *Message) encode(pe packetEncoder) error {
pe.push(newCRC32Field(crcIEEE))
pe.putInt8(m.Version)
attributes := int8(m.Codec) & compressionCodecMask
pe.putInt8(attributes)
if m.Version >= 1 {
if err := (Timestamp{&m.Timestamp}).encode(pe); err != nil {
return err
}
}
err := pe.putBytes(m.Key)
if err != nil {
return err
}
var payload []byte
if m.compressedCache != nil {
payload = m.compressedCache
m.compressedCache = nil
} else if m.Value != nil {
switch m.Codec {
case CompressionNone:
payload = m.Value
case CompressionGZIP:
var buf bytes.Buffer
var writer *gzip.Writer
if m.CompressionLevel != CompressionLevelDefault {
writer, err = gzip.NewWriterLevel(&buf, m.CompressionLevel)
if err != nil {
return err
}
} else {
writer = gzip.NewWriter(&buf)
}
if _, err = writer.Write(m.Value); err != nil {
return err
}
if err = writer.Close(); err != nil {
return err
}
m.compressedCache = buf.Bytes()
payload = m.compressedCache
case CompressionSnappy:
tmp := snappy.Encode(m.Value)
m.compressedCache = tmp
payload = m.compressedCache
case CompressionLZ4:
var buf bytes.Buffer
writer := lz4.NewWriter(&buf)
if _, err = writer.Write(m.Value); err != nil {
return err
}
if err = writer.Close(); err != nil {
return err
}
m.compressedCache = buf.Bytes()
payload = m.compressedCache
default:
return PacketEncodingError{fmt.Sprintf("unsupported compression codec (%d)", m.Codec)}
}
// Keep in mind the compressed payload size for metric gathering
m.compressedSize = len(payload)
}
if err = pe.putBytes(payload); err != nil {
return err
}
return pe.pop()
}通过代码可以看出，gzip压缩的时候，使用了gzip.NewWriter方法。此时已经很明显了。
由于大量的小日志，在写到kafka之前，都在大量的gzip压缩，造成了大量的CPU时间浪费在了GC上。
4： 如何解决？
此时对go熟悉的人都会想起使用sync.pool 复用对象，避免频繁GC。
sarama官方最新的代码：
import (
"bytes"
"compress/gzip"
"fmt"
"sync"
"github.com/eapache/go-xerial-snappy"
"github.com/pierrec/lz4"
)
var (
lz4WriterPool = sync.Pool{
New: func() interface{} {
return lz4.NewWriter(nil)
},
}
gzipWriterPool = sync.Pool{
New: func() interface{} {
return gzip.NewWriter(nil)
},
}
)
func compress(cc CompressionCodec, level int, data []byte) ([]byte, error) {
switch cc {
case CompressionNone:
return data, nil
case CompressionGZIP:
var (
err error
buf bytes.Buffer
writer *gzip.Writer
)
if level != CompressionLevelDefault {
writer, err = gzip.NewWriterLevel(&buf, level)
if err != nil {
return nil, err
}
} else {
writer = gzipWriterPool.Get().(*gzip.Writer)
defer gzipWriterPool.Put(writer)
writer.Reset(&buf)
}
if _, err := writer.Write(data); err != nil {
return nil, err
}
if err := writer.Close(); err != nil {
return nil, err
}
return buf.Bytes(), nil
case CompressionSnappy:
return snappy.Encode(data), nil
case CompressionLZ4:
writer := lz4WriterPool.Get().(*lz4.Writer)
defer lz4WriterPool.Put(writer)
var buf bytes.Buffer
writer.Reset(&buf)
if _, err := writer.Write(data); err != nil {
return nil, err
}
if err := writer.Close(); err != nil {
return nil, err
}
return buf.Bytes(), nil
case CompressionZSTD:
return zstdCompress(nil, data)
default:
return nil, PacketEncodingError{fmt.Sprintf("unsupported compression codec (%d)", cc)}
}
}通过最新的代码可以看出，官方只是在不启用gzip压缩的时候(compressionlevel=-1000)，会复用对象池。
这并不能满足我们的需求。
所以更改以后的代码如下：
package sarama
import (
"bytes"
"compress/gzip"
"fmt"
"sync"
snappy "github.com/eapache/go-xerial-snappy"
"github.com/pierrec/lz4"
)
var (
lz4WriterPool = sync.Pool{
New: func() interface{} {
return lz4.NewWriter(nil)
},
}
gzipWriterPool = sync.Pool{
New: func() interface{} {
return gzip.NewWriter(nil)
},
}
gzipWriterPoolForCompressionLevel1 = sync.Pool{
New: func() interface{} {
gz, err := gzip.NewWriterLevel(nil, 1)
if err != nil {
panic(err)
}
return gz
},
}
gzipWriterPoolForCompressionLevel2 = sync.Pool{
New: func() interface{} {
gz, err := gzip.NewWriterLevel(nil, 2)
if err != nil {
panic(err)
}
return gz
},
}
gzipWriterPoolForCompressionLevel3 = sync.Pool{
New: func() interface{} {
gz, err := gzip.NewWriterLevel(nil, 3)
if err != nil {
panic(err)
}
return gz
},
}
gzipWriterPoolForCompressionLevel4 = sync.Pool{
New: func() interface{} {
gz, err := gzip.NewWriterLevel(nil, 4)
if err != nil {
panic(err)
}
return gz
},
}
gzipWriterPoolForCompressionLevel5 = sync.Pool{
New: func() interface{} {
gz, err := gzip.NewWriterLevel(nil, 5)
if err != nil {
panic(err)
}
return gz
},
}
gzipWriterPoolForCompressionLevel6 = sync.Pool{
New: func() interface{} {
gz, err := gzip.NewWriterLevel(nil, 6)
if err != nil {
panic(err)
}
return gz
},
}
gzipWriterPoolForCompressionLevel7 = sync.Pool{
New: func() interface{} {
gz, err := gzip.NewWriterLevel(nil, 7)
if err != nil {
panic(err)
}
return gz
},
}
gzipWriterPoolForCompressionLevel8 = sync.Pool{
New: func() interface{} {
gz, err := gzip.NewWriterLevel(nil, 8)
if err != nil {
panic(err)
}
return gz
},
}
gzipWriterPoolForCompressionLevel9 = sync.Pool{
New: func() interface{} {
gz, err := gzip.NewWriterLevel(nil, 9)
if err != nil {
panic(err)
}
return gz
},
}
)
func compress(cc CompressionCodec, level int, data \[\]byte) (\[\]byte, error) {
switch cc {
case CompressionNone:
return data, nil
case CompressionGZIP:
var (
err error
buf bytes.Buffer
writer \*gzip.Writer
)
switch level {
case CompressionLevelDefault:
writer = gzipWriterPool.Get().(\*gzip.Writer)
defer gzipWriterPool.Put(writer)
writer.Reset(&buf)
case 1:
writer = gzipWriterPoolForCompressionLevel1.Get().(\*gzip.Writer)
defer gzipWriterPoolForCompressionLevel1.Put(writer)
writer.Reset(&buf)
case 2:
writer = gzipWriterPoolForCompressionLevel2.Get().(\*gzip.Writer)
defer gzipWriterPoolForCompressionLevel2.Put(writer)
writer.Reset(&buf)
case 3:
writer = gzipWriterPoolForCompressionLevel3.Get().(\*gzip.Writer)
defer gzipWriterPoolForCompressionLevel3.Put(writer)
writer.Reset(&buf)
case 4:
writer = gzipWriterPoolForCompressionLevel4.Get().(\*gzip.Writer)
defer gzipWriterPoolForCompressionLevel4.Put(writer)
writer.Reset(&buf)
case 5:
writer = gzipWriterPoolForCompressionLevel5.Get().(\*gzip.Writer)
defer gzipWriterPoolForCompressionLevel5.Put(writer)
writer.Reset(&buf)
case 6:
writer = gzipWriterPoolForCompressionLevel6.Get().(\*gzip.Writer)
defer gzipWriterPoolForCompressionLevel6.Put(writer)
writer.Reset(&buf)
case 7:
writer = gzipWriterPoolForCompressionLevel7.Get().(\*gzip.Writer)
defer gzipWriterPoolForCompressionLevel7.Put(writer)
writer.Reset(&buf)
case 8:
writer = gzipWriterPoolForCompressionLevel8.Get().(\*gzip.Writer)
defer gzipWriterPoolForCompressionLevel8.Put(writer)
writer.Reset(&buf)
case 9:
writer = gzipWriterPoolForCompressionLevel9.Get().(\*gzip.Writer)
defer gzipWriterPoolForCompressionLevel9.Put(writer)
writer.Reset(&buf)
default:
writer, err = gzip.NewWriterLevel(&buf, level)
if err != nil {
return nil, err
}
}
if \_, err := writer.Write(data); err != nil {
return nil, err
}
if err := writer.Close(); err != nil {
return nil, err
}
return buf.Bytes(), nil
case CompressionSnappy:
return snappy.Encode(data), nil
case CompressionLZ4:
writer := lz4WriterPool.Get().(\*lz4.Writer)
defer lz4WriterPool.Put(writer)
var buf bytes.Buffer
writer.Reset(&buf)
if \_, err := writer.Write(data); err != nil {
return nil, err
}
if err := writer.Close(); err != nil {
return nil, err
}
return buf.Bytes(), nil
case CompressionZSTD:
return zstdCompress(nil, data)
default:
return nil, PacketEncodingError{fmt.Sprintf("unsupported compression codec (%d)", cc)}
}
}
生产环境结果
直接上图：
升级前后cpu利用率对比。
总结
1: PProf 是个性能调优的大杀器。
2: 其实filebeat 还有更多的优化点。比如json 序列化。
3：实际结果cpu使用降低了一半，采集速度却提高了20%。
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