Posted on:April 1, 2019 at 09:00 AM

Redis Conference | Traninig Workshop

Redis Conference | Traninig Workshop 
TRAINING SCHEDULE
9:00 - 10:00 am - Redis
9:45 - 11:15 am - Redis as a Primary Data Store
11:25 - 12:55 pm - Redis + K8s
1:55 pm - 3:25 pm - Deep Learning with Redis
3:35 pm - 5:05 pm - Streaming Architectures

EVENT INFORMATION

Redisconf 2019

Training Day: April 1, 2019
Conference Days: April 2 - 3, 2019

VENUE

Pier 27
The EmbarcaderoSan Francisco, CA 94111

PRESENTED BY

Redis Labs, Inc
[email protected]

0900..1000 Redis Introduction

redis-cli
> ping
pong

telnet localhost 6379
> ping
+PONG

> SET mykey somevalue
> GET mykey
"somevalue"
  • key1.2.3 -> map to complex data structure
  • flat key-value space
  • value are more complex: list, hash, …
> set myobject:1 "ksdfsd dsf sdf sdfsdf sdf "
> get myobject:1

1) hash

redis hmset: key-value type data

> hmset myobejct:100 user antirez id 1234 age 42
> hgetall myobject:100
1) "user"
2) "antirez"
3) "id"
4) '1234"
5) "age"
6) "42"
> hget myobject:100 id
"1234"
> hget myobject:100 user
"antirez"

2) list

> rpush mylist 1
> rpush mylist 2
> rpush mylist 34 5 6 7
> lrange mylist 0 -1
1) "1"
2) "2"
...
7) "7"
> lpush mylist a b c
> lrange mylist 0 -1
1) "c"
2) "b"
3) "a"
4) "1"
...
11) "7"
> lpop mylist
"c"
> lpop mylist
"b"
> rpop mylist
"7"
> rpop mylist
"6"
> lrange mylist 0 -1
1) "1"
2) "2"
3) "3"
4) "4"
> RPOPLPUSH mylist mylist
"4"
> lrange mylist 0 -1
1) "4"
2) "1"
3) "2"
4) "3"
> RPOPLPUSH mylist mylist
"3"
> lrange mylist 0 -1
1) "3"
2) "4"
3) "1"
4) "2"
  • rpoplpush is really good
> set object:1234 foo
> set object:1234 bar
> get object:1234
"bar"
  • to retain the history
> del object:1234
> lpush object:1234 foo
> lpush object:1234 bar
> larnge object:1234 0 0
"bar"
> lrange object:1234 0 -1 # to see the history
1) "bar"
2) "foo"
> ltrim object:1234 0 1
> lrange object:1234 0 -1
1) "foo"

> lpush A username:239:salvatore
> lpush B username:239:salvatore
> lpush A username:03232:anna
> lrange A 0 -1
1) username:093:anna
2) username:239:salvatore
> lrange B 0 -1
1) "username:230:salvatore"

3) sorted set

  • good for leader board
> zadd myzset 1.2 A 3.2 B 5 C  # element-value
> zrange myzset 0 -1
1) "A"
2) "B"
3) "C"
> zadd myzset 0.5 C
> zrange myzset 0 -1
1) "C"
2) "A"
3) "B"
> zrange myzset 0 -1 WITHSCORES
1) "C"
2) "0.5"
3) "A"
4) "1.2"
5) "B"
6) "3.2"
> del myzset
> zadd myzset 100 antirez 5 trent 2000 anna
> zrange myset 0 -1 WITHSCOPE
1) "trent"
2) "5"
3) "antirez"
4) "100"
5) "anna"
6) "2000"
> ZRANK myzset anna
2
> ZRANK myzset trent
0
> ZRANK myzset antirez
1
  • pop the element
> zpopmin myzset
1) "trent"
2) "5"
> zpopmin myzset
1) "antirez"
2) "100"
> zpopmin myzset
1) "anna"
2) "2000"
> zpopmin myzset
(nil)

1000..1115 Redis as a Primary Data Store

Overview & Introduction

  • redis can be your primary database not just as a cache
  • reids is your single source of truth
  • redis is your operational database
  • database/datastore for disaster recovery
  • this requires:
    • proper configuration of redis
    • suitable data
    • proper data structuring
  • Single Source of Truth
    • you write and read from redis directly
    • there is no database that defines what is true to your application
    • you may have other methods to define truth for archival or non-working set
      • data shouldn’t be operated on from more than one place
    • Advantages:
      • Speed
      • Low application complexity
      • Low architectural complexity
    • Disadvantages:
      • Mistrust
      • Data is not always suitable

Transaction

  • ACID transaction
    • Atomicity Consistency Isonation Durability
  • redis is single threaded
  • Signle Client - Execution flow
  • two client - execution flow
  • multiple command transaction
    • MULTI to start transaction block
    • EXEC to close transaction block
    • commands are queued until exec
    • all commands or no commands are applied
    • transaction
> MULTI
> sadd site:visitors 125
> incr site:raw-count
> hset sessions:124 userid salvatore ip 127.0.0.1
> EXEC
  • discard example
> DISCARD
  • transa ction with errors - syntatic error
> EXEC
(error)EXECABORT Transaction discarded because of previous errors.
  • transaction with errors - semantic error
> EXEC
  • conditional execution/ optimistic concurrency control
    • use WATCH, UNWATCH
  • dependent modifications - incorrect version
  • dependent modifications - correct version
python code
tx = r.pipeline()
tx.watch(debitkey) <== **
tx.multi()
tx.hincrbyfloat
tx.execute()

except WatchError: <== **
  # watched 'balance' value had changed
  # so retry
  • disk based persistence - options
    • RDB = snapshot: store a compact point-in-time copy every 30m, hourly
    • AOF = append only files: write to disk(fsync) every second or every write -tunable
  • RDB Persistence
    • Persistence
      • fork redis rpocess
      • child process wirtes new RDB file
      • atomic replace old RDB file with new
    • Trigger manually
      • SAVE command(synch)
      • BGSAVE(background)
  • AOF Presisteance
    • configuration
      • appendonly directly (reids.conf): APPENDONLY YES
      • runtimeL CONFIG SET APPENDONLY YES
    • AOF FILE fsync options
      • trace off speed for data security
      • options: none, every second, always
    • BGREWRITE
  • Redis enterprise durability - persistence topologies
    • master replica, writing from replica is slow
    • master replica, wrting from master is faster but still slow, tunable
  • Transactions Summary
    • no rollbacks
    • rollback- free transcations, different beast from rollback transactions
      • atomic
      • isolation, consisteency
      • durability
    • transaction work differently than other databases, archives the same goals
    • single threaded event-loop for serving commands
    • WATCH for optimistic concurrenty control

Data Structuring

  • hands on
  • reids is a building block
  • Ordered data
    • each row is a hash
    • rows can be accessed by a key
    • hashes manage any type of ordering or index
  • Add an item
    • with sorted set and hashes
MULTI
HSET people:1234 first kyle last davis birthyear 1981
ZADD people:by-birthyear 1981 people:1234
EXEC
  • MULTI/EXEC clustering trade-off
  • Read an item
HGETALL people:1234
# HGET people:1234
# HMGET people:1234
  • LIST items
ZRANGEBYSCORE people:by-birthyear 1980 1990
1) people
...
> HGETALL people:1234
  • lua is redis’s built in script language
  • UPDATE item
MULTI
HSET people:1234 birthyear 1982
ZADD people:by-birthyear 1982 people:1234
EXEC
  • DELETE item
MULTI
UNLINK people:1234
ZREM people:by-birthyear people:1234
EXEC

Clustering, keys & transactions

  • 4 Shard/ Node Cluster
  • MULTI/EXEC
  • something could die during transaction going to different shards
  • how to prevent this
  • Data layout - isolated data
    • data assoicated with one company/account/etc
    • operationally, you may not need any non-isolated access
  • Keep all the data together with key “tags” using curly braces
c:{xyz}:people:1234
c:{xyz}:people:by-birthyear
# hash('xyz') = n => will alwyas go to the same shard
  • Without MULTI/EXEC and non-isolated data
    • trade offs and complexity but still possible
    • non-critical data/application
      • always manipulate your data first
        • hset/unlink/del/etc
      • then manipulate index
      • your application needs to be able to handle missing data and incorrect indexes
      • possible index cleanup tasks with background SCAN commands
    • event pattern
      • data changes are written as a series of changes adding to a stream
      • indexes are gneerated
  • Easier with “RediSearch”
# init
FT.CREATE people-index SCHEMA birthyear number last text first text
# add
HSET people:1234 birthyear 1881 last davis first kyle
FT.ADDHASH people-index people:
# update


# add
FT.GET

# LIST
FT.SEARCH people-index "@birthyear:[1980 1990]

# DELETE
FT.DEL
  • redis search takes care of the clustering*!
  • you don’t have to worry about it
  • clusering is only availble on enterprise edition

Examples:

  • Data suitablitiy / Non-relational data
    • can your data be primariliy referenced by a key?
    • Does your data’s structure vary widely?
    • How normalized is your data?
    • Is your data naturally isolated by particular
  • Denormalized Data
    • data is stored the way it will be retrived
    • Breaks the rule of repeated data
      • lower compelxity
  • Bad example: Higly isolated data
    • student record system at a universy
    • One screen that displays
    • highly relational -> don’t build it with redis
  • Good example: Student communication Portal

1130..0100 Redis + K8s

  • Redisconf2019 google group
  • Why?
    • Brendan Burns: k8s founder, kubecon 15’ keynote
    • “We’re making reliable, scable, agile distributed systems a CS101 exercise”
  • How?
    • Kubernetes speeds up the development process by:
    • making deployments easy
    • automating deployments
    • simplifying updates/upgrades
  • What?
  • Where?
    • 54% of fortune 500 companies - business insider 2019, Jan

K8s concepts

  • what k8s can do for me? workloads.
    • run a workload consisting fo docker containers
    • manage a cluster of many nodes (aka hosts, instances)
    • spread the workload across the nodes intelligently
    • dispatch incoming traffic to the relevant backend (ingress)
  • what k8s can do for me? management
    • monitor services and restart them as needed
    • manage scaling up/down services
    • manage deployment of new versions in various ways
  • how does it owrk?
    • api/ master nodes/ worker nodes
    • inptu by kubectl/ webui
    • master nodes/ api server, scheduler, controllers, etcd
    • workder nodes: pods, docker, kubelet, kube-proxy
kubectl get pods
kubectl apply -f pod-redis.yaml
kubectl get pods -o wide
# in yaml
command: ['sleep', 'infinity']
kubectl apply -f pod-client.yaml
kubectl get pods
kubectl get -it redis bash
# inside of the pod
ps
cat /proc/1/cmdline
redis-cli
keys *
set hello world
exit
kubectl exec -it client bash
# need to know redis address
kubectl get pods -o wide
# read IP, 10.42.2.117
kubectl exec -it client bash
redis-cli
commad not fine
nc -v 10.40.2.117 6379
get hello
world
vi service-redis.yaml
kubectl apply -f service-redis.yaml
vi pod-redis.yaml
# add labels: app: redis
kubectl apply -f pod-redis-labels.yaml
kubectl exec -it client bash
nc -v redis 6379 # without ip address
redis.default.svc.cluster.local [10.43.253.180] 6379(?) open
get hello
world
kubectl delete all --all --grace-period
vi deployment-redis.yaml
kbuectl get po
kubectl get events
kubectl create -f deployment-redis.yaml
kubectl scale deployment redis --replicas 3
kubectl get po
kubectl scale deployment redis --replicas 1
vi deployments-redis.yaml
# upgrade from 5.0.3 to 5.0.4
# change the version to 5.0.4
kubectl apply -f deployment-redis.yaml

Workshop: Guestbook demo on github

gcloud beta container clusters get-credentials standard-cluster-1 --region us-west1 --project proven-reality-226706
# turn off tmux settings
# activate cloud shell
git clone https://github.com/amiramm/guestbook-demo
# create namespace
kubectl get namespaces
vi guestbook-demo/namespace.json
# metadata
labels: name: "amizne"
kubectl apply -f guestbook-demo/namespace.json
kubectl get namespaces --show-labels
kubectl config view
# need context and user
kubectl config current-context
kubectl config set-context amizne --namespace=amizne --cluster=gke_redisconf19-k8s-001_us-west1_standard-cluster-1
kubectl config use-context amizne
watch -n 5 kubectl get pods
kubectl apply -f guestbook-demo/redis-master-deployment.yaml

Operator

  • customer resource definition
kind: CustomerResourceDefinition
spec:
  group: appr.redislab.com
  • for redis enterprise customers
  • Custom Resource + Customer Controller = Operator
  • Stateful Set Controller
  • Redis Enterpirse Cluser (REC) Controller
    • can upgrade Redis Enterprise
kubectl get crd
kubectl apply -f crd.yaml
kubectl get all
kubectl apply -f redis-enterprise-cluster.yaml
  • What operator Gives You
    • life cycle control
    • simple configuration/reconfiguration
    • simple deployment
    • k8s native solution
    • automates complex operations

0200..0330 Serving Deep Learning Models at Scale with RedisAI

  • Deep Learning with Redis
  • Luca Antiga, [tensor]werk, CEO
    • Orobix co-founder, PyTorch contributor, Co-author of Deep Learning with PyTouch
  • Agenda
    • deep learning basics
    • RedisAI: architecture, api, operation
    • Hands-on: image recognition and text generation
  • Appsembler: virtual host

Deep learning basics

  • The era of data-driving
    • program whose behiviour is defined by data
    • new era for software development
    • needs to leverage on best practices of software engineering
    • from data scientists to developers
  • Deep learning
    • deep neural networks approximate functions based on examples
    • Pervasive
    • Solves problems that looked very hard a few years back
    • Limit is the data, not the programmers(kinda)
    • Not AGI, but a tool with superpowers
    • image with ladies selling vegetables => Vision deep CNN => Laguage generating RNN => A group of people shopping at outdoor market, There are many vegetables at the fruit stand.
  • Deep learning frameworks
    • Tensorflow, PyTouch, mxnet, CNTK, Chainer, DyNet, DL4J, Flux
    • once the model is trainined, run it on production
    • Production (ONUX, JIT, Tourchscript)
  • Deep Learning 101 - Tensors
    • tensor = multi-dimentional array
    • scalar 0d
    • vector 1d
    • matrix 2d
    • tensor 3d..
    • the main data structure in deep learning
    • multidimensional arrays: arrays that can be indexed using more than one index
    • memory is still 1D
    • it’s just a matter of access and operator semantics
  • Deep Learning 101 - Models
    • operation on tensors to produce other tensors
    • operations are layed out in a comutation graph
    • a graph is like a VM IR with an op-set limited to tensor ops
    • E.g.
      • linear transformation(w*x + b)
        • non-linearity: gamma(w*x + b) * heaviliy nested
  • Deep Learning 101 - Learning
    • Optimizing weights
  • So you’ve got your model trained
    • Now what?
  • Production strategies
    • Python code behind: e.g. Flask
    • Execution service from cloud provider
    • Runtime
      • tensorflow serving
      • clipper
      • nvidia tensorRT inference server
      • MNNet Model server
    • Bespoke solutions(C++, ..)
    • jupyter, flask, python, docker
    • Model server architecture
  • Production requirements
    • must fin the technology stack
    • not just about laugages, but about semantics, scalability, guarantees
    • run anywhere, any size
    • composable building blocks
    • must try to limit the amount of moving parts
    • and the amount of moving data
    • must make best use of resources
  • RedisAI
    • a redis module providing
    • tensors as a data type
    • and depp learning model execution
    • on cpu and gpu
    • it turns redis into a full-fledged depp learning runtime
    • while still being redis
    • new data type: * tensor
    • ONNX is a standard format for neural network
    • ONNX-ML: just machine learning
    • TorchScript
    def addsq(a, b):
  return (a + b)**2
  • Archituecure
    • tensor: framework-agnostic
    • queue + procesing theread
      • backpressure
      • redis stays reponsive
    • models are kept hot in memory
    • client blocks
  • Where to get it 
$docker run -p 6379:6379 -it -rm redisai/redisai

or

git clone [email protected]:RedisAI/RedisAI.git
bash get_deps.sh
mkdir build && cd build
cmake -DDEPS_PATH=../deps/install .. && make
cd ..
redis-server --loadmodule bild/redisai.so
  • git LFS support
  • installs cuda
  • set up some envs
bash get_deps.sh cpu # cpu only
LD_LIBRARY_PATH=`pwd`/deps/install/libs redis-server --loadmodule build/redisai.io

  • API: Tensor

    • AI.TENSORSET
    • AI.TENSORGET
    AI.TENSORSET foo FLOAT 2 2 VALUES 1 2 3 4
AI.TENSORSET foo FLOAT 2 2 BLOB < buffer.new

AI.TENSORGET foo BLOB
AI.TENSORGET foo VALUES
AI.TENSORGET foo META

  • API: Model

    • AI.MODELSET
    AI.MODELSET resnet18 TORCH GPU < foo.pt
AI.MODELSET resnet18 TF CPU INPUTS in1 OUTPUTS linear4 < foot.pt

    AI.MODELRUN resnet18
    • how to export models
      • tensorflow(+keras) : freeze graph

  • API: Script

    • AI.SCRIPTSET
    • AI.SCRIPTRUN
    AI.SCRIPTSET mysadd2 GPU < addtwo.txt
AI.SCRIPTRUN myadd2 addtwo INPUTS foo OUTPUTS BAR

    addtwo.txt

    def addtwo(a, b):
  return a + b

  • Scripts?

    • SCRIPT is a TorchScript interpreter
    • Python-like syntax for tensor ops
    • on CPU and GUP

Hands-on

  • create two 3x3 tensors
  • create SCRIPT that performas element-wise multiply
  • set it
  • run it
# make mymul.txt
def mymul(a, b):
  return a*b

# run redis-cli
AI.TENSORSET foo FLOAT 2 2 VALUES 1 1 2 2
AI.TENSORGET foo VALUES
AI.TENSORGET foo BLOB
AI.TENSORSET bar FLOAT 3 3 VALUES 2 2 2 2 2 2 2 2 2
AI.SCRIPTSET baz CPU ""

# from outside to load script
redis-cli -x AI.SCRIPTSET baz CPU < mul.txt

# after loading
AI.SCRIPTRUN baz mymul INPUTS foo bar OUTPUTS jar

AI.TENSORGET jar values

wget https://s3.amazonaws.com/redisai/redisai-examples.tar.gz
tar -xvfz redisai-exampels.tar.gz
cd redisai-examples/js_client
npm install jimp ioredis

0340..0450 Streaming Architectures

  • Redis Streams
    • were introduced in redis v5
    • necessity is the mother of invention
    • are (yet another) redis data structure <- you can store data in it!
    • are a storage absraction for log-like data
    • can also provide communication between processes
    • are the most complex/robust data redis structure(to date)
  • What is a stream?
    • append-only lis of entries called messages
    • messages are added to a stream by “producers”.
    • every message has an ID that uniquely identifies it, embeds a notion of time and is immutable.
    • order of the messages is imuutable
    • a messsage contains data is a hash-like (field-value pairs) structure
    • data is immutable, but messages can be deleted from the stream
    • messges can be read from the stream by id or by ranges
XACK
XADD
XCLAIM
XDEL
XGROUP
XINFO
XLEN
XPENDING
XRANGE
XREAD
XREADGROUP
XREVRANGE
XTRIM

Hands on

127.0.0.1:6379> keys *
(empty list or set)
127.0.0.1:6379> xadd mystream * foo bar
"1554159172959-0" # millisecret
127.0.0.1:6379> xadd mystream * foo bar
"1554159184276-0"
127.0.0.1:6379> 5 xadd s1 * foo bar  # if you add number in front, it runs that times
"1554159364534-0"
"1554159364535-0"
"1554159364583-0"
"1554159364583-1"  # same milliseconds, sequences
"1554159364583-2"  # same milliseconds, sequences
> 500 xadd mystream * foo bar
127.0.0.1:6379> xlen mystream
(integer) 507
127.0.0.1:6379> xtrim mystream maxlen ~ 123
(integer) 303
127.0.0.1:6379> xlen mystream
(integer) 204

streamsworkshop$ python producer_1.py
1554159899.914: Stream numbers has 0 messages and uses None bytes
1554159899.915: Produced the number 0 as message id 1554159899914-0
1554159900.917: Stream numbers has 1 messages and uses 581 bytes
1554159900.917: Produced the number 1 as message id 1554159900916-0
1554159901.919: Stream numbers has 2 messages and uses 592 bytes

redis-cli flushall
  • code: producer_2.py
127.0.0.1:6379> XINFO STREAM numbers
 1) "length"
 2) (integer) 47
 3) "radix-tree-keys"
 4) (integer) 1
 5) "radix-tree-nodes"
 6) (integer) 2
 7) "groups"
 8) (integer) 0
 9) "last-generated-id"
10) "1554160089688-0"
11) "first-entry"
12) 1) "1554160043587-0"
    2) 1) "n"
       2) "0"
13) "last-entry"
14) 1) "1554160089688-0"
    2) 1) "n"
       2) "46"

127.0.0.1:6379> XRANGE numbers - + COUNT 5
1) 1) "1554160043587-0"
   2) 1) "n"
      2) "0"
2) 1) "1554160044589-0"
   2) 1) "n"
      2) "1"
3) 1) "1554160045591-0"
   2) 1) "n"
      2) "2"
4) 1) "1554160046593-0"
   2) 1) "n"
      2) "3"
5) 1) "1554160047595-0"
   2) 1) "n"
      2) "4"
127.0.0.1:6379> XRANGE numbers 1554160047595-0 + COUNT 5
1) 1) "1554160047595-0"
   2) 1) "n"
      2) "4"
2) 1) "1554160048598-0"
   2) 1) "n"
      2) "5"
3) 1) "1554160049600-0"
   2) 1) "n"
      2) "6"
4) 1) "1554160050602-0"
   2) 1) "n"
      2) "7"
5) 1) "1554160051604-0"
   2) 1) "n"
      2) "8"
  • code: range_1.py
127.0.0.1:6379> XRANGE numbers 1554159977896-2 1554159977896-2
(empty list or set)
  • code: range_sum.py
streamsworkshop$ python range_sum.py
The running sum of the Natural Numbers Stream is 9045 (added 135 new numbers)
streamsworkshop$ python range_sum.py
The running sum of the Natural Numbers Stream is 9045 (added 0 new numbers)

127.0.0.1:6379> hgetall numbers:range_sum
1) "last_id"
2) "1554160177886-1"
3) "n_sum"
4) "9045"

127.0.0.1:6379> XREAD STREAMS numbers 0
1) 1) "numbers"
   2)   1) 1) "1554160043587-0"
           2) 1) "n"
              2) "0"
        2) 1) "1554160044589-0"
           2) 1) "n"---
127.0.0.1:6379> XREAD COUNT 5 STREAMS numbers 1554160177486-0
1) 1) "numbers"
   2) 1) 1) "1554160177886-0"
         2) 1) "n"
            2) "134"
127.0.0.1:6379> XREAD BLOCK 100 STREAMS numbers $
(nil)
  • code: blockingread_sum.py
127.0.0.1:6379> XGROUP CREATE numbers grp 0
OK
127.0.0.1:6379> XREADGROUP GROUP grp c1 STREAMS numbers 0
1) 1) "numbers"
   2) (empty list or set)
127.0.0.1:6379> XREADGROUP GROUP grp c1 COUNT 1 STREAMS numbers >
1) 1) "numbers"
   2) 1) 1) "1554160043587-0"
         2) 1) "n"
            2) "0"
127.0.0.1:6379> XINFO GROUPS numbers
1) 1) "name"
   2) "grp"
   3) "consumers"
   4) (integer) 2
   5) "pending"
   6) (integer) 2
   7) "last-delivered-id"
   8) "1554160044589-0"
127.0.0.1:6379> XINFO CONSUMERS numbers grp
1) 1) "name"
   2) "c1"
   3) "pending"
   4) (integer) 1
   5) "idle"
   6) (integer) 99060
2) 1) "name"
   2) "c2"
   3) "pending"
   4) (integer) 1
   5) "idle"
   6) (integer) 60502
127.0.0.1:6379> XACK numbers grp 1554161747918-0
(integer) 0
127.0.0.1:6379> XINFO CONSUMERS numbers grp
1) 1) "name"
   2) "c1"
   3) "pending"
   4) (integer) 1
   5) "idle"
   6) (integer) 63254
2) 1) "name"
   2) "c2"
   3) "pending"
   4) (integer) 1
   5) "idle"
   6) (integer) 176549
127.0.0.1:6379> XPENDING numbers grp
1) (integer) 195
2) "1554160043587-0"
3) "1554161864418-0"
4) 1) 1) "c1"
      2) "194"
   2) 1) "c2"
      2) "1"
127.0.0.1:6379> XPENDING numbers grp - + 10
 1) 1) "1554160043587-0"
    2) "c1"
    3) (integer) 194762
    4) (integer) 2
...

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